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.
翻译:控制属性的文本重写,也称为文本样式转换,在调控文本培训数据和机器生成文本的属性和偏差方面具有关键作用。在这项工作中,我们展示了简单Style,这是一个由两个简单元素组成的最起码但有效的风格转移方法,由两个简单元素组成:受控除去和输出过滤。尽管我们的方法简单,可以用几行代码简洁地描述,但在自动和人文评估方面,它与以前最先进的质量转换方法具有竞争力。为了证明我们的系统除了学术数据外的适应性和实际价值,我们应用简单Style从社交网络中传输大量真实世界文本数据中的文本属性。此外,我们引入了一种新型的“软禁”技术,以进一步改进我们的系统性能。我们还表明,教授学生模式产生简单Style的输出,可以导致一个系统,在自动和人文评估中只使用一个贪婪分解的样本进行等同质量的风格转移。最后,我们建议我们的方法作为一种补救措施,用于解决在实地取得进展的基本互不相容的基线问题。我们提供协议,作为一种简单但又强有力的基准版本的属性,用于外地工作的递增缩。