SimpleStyle: An Adaptable Style Transfer Approach

• URL: http://arxiv.org/abs/2212.10498v2
• Date: Thu, 22 Dec 2022 16:26:36 GMT
• Title: SimpleStyle: An Adaptable Style Transfer Approach
• Authors: Elron Bandel, Yoav Katz, Noam Slonim, Liat Ein-Dor
• Abstract summary: We present SimpleStyle, a minimalist yet effective approach for style-transfer composed of two simple ingredients: controlled denoising and output filtering. We apply SimpleStyle to transfer a wide range of text attributes appearing in real-world textual data from social networks. We 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.
• Score: 6.993665837027786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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