  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.
