Attribute-controlled text rewriting, also known as text style-transfer, has a
crucial role in regulating attributes and biases of textual training data and a
machine generated text. In this work we present SimpleStyle, a minimalist yet
effective approach for style-transfer composed of two simple ingredients:
controlled denoising and output filtering. Despite the simplicity of our
approach, which can be succinctly described with a few lines of code, it is
competitive with previous state-of-the-art methods both in automatic and in
human evaluation. To demonstrate the adaptability and practical value of our
system beyond academic data, we apply SimpleStyle to transfer a wide range of
text attributes appearing in real-world textual data from social networks.
Additionally, we introduce a novel "soft noising" technique that further
improves the performance of our system. We also show that teaching a student
model to generate the output of SimpleStyle can result in a system that
performs style transfer of equivalent quality with only a single greedy-decoded
sample. Finally, we suggest our method as a remedy for the fundamental
incompatible baseline issue that holds progress in the field. We offer our
protocol as a simple yet strong baseline for works that wish to make
incremental advancements in the field of attribute controlled text rewriting.

本文提出了 SimpleStyle 方法，它包含了受控去噪和输出过滤两个简单部分，从而有效地实现了文本风格转换，并通过实验验证了其有效性。同时，作者还引入了一种名为 “soft noising” 的新技术来进一步改进系统性能，并将其应用于社交网络中真实文本数据的风格转换。作者建议 SimpleStyle 方法可作为属性控制文本重写领域的基准方法。

SimpleStyle：一种可适应的风格转移方法

SimpleStyle: An Adaptable Style Transfer Approach

Machine generated text is increasingly difficult to distinguish from human
authored text. Powerful open-source models are freely available, and
user-friendly tools that democratize access to generative models are
proliferating. ChatGPT, which was released shortly after the first preprint of
this survey, epitomizes these trends. The great potential of state-of-the-art
natural language generation (NLG) systems is tempered by the multitude of
avenues for abuse. Detection of machine generated text is a key countermeasure
for reducing abuse of NLG models, with significant technical challenges and
numerous open problems. We provide a survey that includes both 1) an extensive
analysis of threat models posed by contemporary NLG systems, and 2) the most
complete review of machine generated text detection methods to date. This
survey places machine generated text within its cybersecurity and social
context, and provides strong guidance for future work addressing the most
critical threat models, and ensuring detection systems themselves demonstrate
trustworthiness through fairness, robustness, and accountability.

本文是关于机器生成文本和自然语言生成领域的综述。研究表明，机器生成文本与人类作者的文本越来越难以区分，因此我们讨论了现代自然语言生成系统带来的威胁模型，并对文本检测方法进行了最全面的综述。本文进一步讨论了机器生成文本的安全和社会背景，并提出了在未来工作中需要考虑的最关键威胁模型，以及确保检测系统本身通过公正、稳健和负责任的方式展示其信任性。