Reformulating Unsupervised Style Transfer as Paraphrase Generation

Understand

Modern NLP defines the task of style transfer as modifying the style of a given sentence without appreciably changing its semantics, which implies that the outputs of style transfer systems should be paraphrases of their inputs.

• However, many existing systems purportedly designed for style transfer inherently warp the input's meaning through attribute transfer, which changes semantic properties such as sentiment.
• In this paper, we reformulate unsupervised style transfer as a paraphrase generation problem, and present a simple methodology based on fine-tuning pretrained language models on automatically generated paraphrase data.
• Despite its simplicity, our method significantly outperforms state-of-the-art style transfer systems on both human and automatic evaluations.