Textual style transfer is the task of transforming stylistic properties of
text while preserving meaning. Target "styles" can be defined in numerous ways,
ranging from single attributes (e.g, formality) to authorship (e.g,
Shakespeare). Previous unsupervised style-transfer approaches generally rely on
significant amounts of labeled data for only a fixed set of styles or require
large language models. In contrast, we introduce a novel diffusion-based
framework for general-purpose style transfer that can be flexibly adapted to
arbitrary target styles at inference time. Our parameter-efficient approach,
ParaGuide, leverages paraphrase-conditioned diffusion models alongside
gradient-based guidance from both off-the-shelf classifiers and strong existing
style embedders to transform the style of text while preserving semantic
information. We validate the method on the Enron Email Corpus, with both human
and automatic evaluations, and find that it outperforms strong baselines on
formality, sentiment, and even authorship style transfer.

文本样式转换是将文本的样式特征进行转换而保留其含义的任务。我们引入了一种新颖的基于扩散的通用样式转换框架 ParaGuide，它可以在推理时灵活地适应任意目标样式，利用改进的扩散模型，结合来自现成分类器和强大的已有样式嵌入器的梯度引导，以转换文本的样式同时保留语义信息。我们在 Enron Email Corpus 上进行人工和自动评估，发现该方法在正式化、情感以及甚至作者风格转换方面均超越了强大的基准。

ParaGuide: 文本风格转换的引导扩散改写器

ParaGuide: Guided Diffusion Paraphrasers for Plug-and-Play Textual Style  Transfer

We propose a method for arbitrary textual style transfer (TST)--the task of
transforming a text into any given style--utilizing general-purpose pre-trained
language models. Our method, Prompt-and-Rerank, is based on a mathematical
formulation of the TST task, decomposing it into three constituent components:
textual similarity, target style strength, and fluency. Specifically, our
method first uses zero-shot or few-shot prompting to obtain a set of candidate
generations in the target style, and then re-ranks these candidates according
to a combination of the three components above. Empirically, our method enables
small pre-trained language models to perform on par with state-of-the-art
large-scale models while consuming two orders of magnitude less compute and
memory. Finally, we conduct a systematic investigation of the effect of model
size and prompt design (e.g., prompt paraphrasing and delimiter-pair choice) on
style transfer quality across seven diverse textual style transfer datasets.

论文提出了 Prompt-and-Rerank 方法，利用预训练语言模型实现任意文本风格转换，并在七个不同的数据集上进行了实证研究，探究模型大小和提示设计对风格转移质量的影响。

Prompt-and-Rerank: 小语言模型的零 / 少样式转换方法

Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models

The difficulty of textual style transfer lies in the lack of parallel
corpora. Numerous advances have been proposed for the unsupervised generation.
However, significant problems remain with the auto-evaluation of style transfer
tasks. Based on the summary of Pang and Gimpel (2018) and Mir et al. (2019),
style transfer evaluations rely on three criteria: style accuracy of
transferred sentences, content similarity between original and transferred
sentences, and fluency of transferred sentences. We elucidate the problematic
current state of style transfer research. Given that current tasks do not
represent real use cases of style transfer, current auto-evaluation approach is
flawed. This discussion aims to bring researchers to think about the future of
style transfer and style transfer evaluation research.

本文讨论了文本风格迁移技术中的关键问题，即使用无监督生成方法，自动评估风格迁移任务的难点。通过对类似文献的总结，我们阐述了当前风格迁移研究的问题，并指出现有的自动评估方法存在缺陷，无法准确评估迁移后句子的风格准确性、内容相似度和流畅性。本文旨在引导研究者思考风格迁移和评估研究的未来趋势。