提示学习启发的无监督情感风格迁移研究OA北大核心CSTPCD

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.