计算机工程与应用2024，Vol.60Issue(5)：146-155,10.DOI:10.3778/j.issn.1002-8331.2211-0317
提示学习启发的无监督情感风格迁移研究
Prompt-Learning Inspired Approach to Unsupervised Sentiment Style Transfer
摘要
Abstract
Text style transfer is the task of transferring text generation with certain desired style properties while preserving the original text content.In order to improve the transfer quality under non-parallel style corpus,this paper proposes a new method to guide the fill-mask model to rewrite the sentence into the target style.Overall,this approach is based on the delete-retrieve-generate style transfer framework,but employs a large unsupervised pre-trained language model and Transformer architecture.According to the working principle of Transformer,firstly,the method of filtering style attri-butes from the source sentence is improved,and then the internal knowledge of the pre-trained model is mined by the prompt learning method to generate the target style words.Experiments on two sentiment benchmark datasets show that the method outperforms existing editing methods,with an average improvement of more than 14% in relative scores on the comprehensive metrics.关键词
文本生成/文本样式迁移/情感迁移/提示学习/TransformerKey words
text generation/text style transfer/sentiment transfer/prompt learning/Transformer分类
信息技术与安全科学引用本文复制引用
蔡国永,李安庆..提示学习启发的无监督情感风格迁移研究[J].计算机工程与应用,2024,60(5):146-155,10.基金项目
国家自然科学基金(62366010) （62366010）
广西可信软件重点实验室项目(kx202060). （kx202060）
