  Prompting approaches have been recently explored in text style transfer,
where a textual prompt is used to query a pretrained language model to generate
style-transferred texts word by word in an autoregressive manner. However, such
a generation process is less controllable and early prediction errors may
affect future word predictions. In this paper, we present a prompt-based
editing approach for text style transfer. Specifically, we prompt a pretrained
language model for style classification and use the classification probability
to compute a style score. Then, we perform discrete search with word-level
editing to maximize a comprehensive scoring function for the style-transfer
task. In this way, we transform a prompt-based generation problem into a
classification one, which is a training-free process and more controllable than
the autoregressive generation of sentences. In our experiments, we performed
both automatic and human evaluation on three style-transfer benchmark datasets,
and show that our approach largely outperforms the state-of-the-art systems
that have 20 times more parameters. Additional empirical analyses further
demonstrate the effectiveness of our approach.
