SimpleStyle: An Adaptable Style Transfer Approach
Elron Bandel, Yoav Katz, Noam Slonim, Liat Ein-Dor

TL;DR
SimpleStyle is a minimalist yet effective style transfer method using controlled denoising and output filtering, competitive with state-of-the-art approaches, applicable to real-world data, and introducing a novel soft noising technique.
Contribution
It presents a simple, adaptable style transfer approach with a new soft noising technique, demonstrating competitive performance and practical applicability beyond academic datasets.
Findings
Competitive with state-of-the-art methods in automatic and human evaluations.
Effective in transferring a wide range of real-world text attributes.
Enables training a student model for efficient style transfer.
Abstract
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…
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Taxonomy
TopicsSpeech Recognition and Synthesis · Topic Modeling · Natural Language Processing Techniques
