Prompt-Based Editing for Text Style Transfer

• URL: http://arxiv.org/abs/2301.11997v2
• Date: Fri, 22 Dec 2023 05:49:33 GMT
• Title: Prompt-Based Editing for Text Style Transfer
• Authors: Guoqing Luo, Yu Tong Han, Lili Mou, Mauajama Firdaus
• Abstract summary: We present a prompt-based editing approach for text style transfer. We transform a prompt-based generation problem into a classification one, which is a training-free process. Our approach largely outperforms the state-of-the-art systems that have 20 times more parameters.
• Score: 25.863546922455498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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