计算机技术与发展2023，Vol.33Issue(12)：156-162,7.DOI:10.3969/j.issn.1673-629X.2023.12.022
基于多掩码与提示句向量融合分类的立场检测
Stance Detection Based on Multi-mask and Prompt Sentence Vector Fusion Classification
摘要
Abstract
Stance detection refers to the analysis of the stance expressed by the text on a target topic,which usually includes support,against and none.Existing works mostly use methods such as BERT to extract sentence feature vectors of the text and topic,and usually,the first token hidden state or the average of the hidden states of each word in the sentence is used as the sentence vector.We improve the acquisition of sentence vectors by using prompt learning templates to obtain prompt sentence vectors and enhance the feature extraction effect of sentence vectors.A stance detection model based on multiple masks and prompt sentence vector fusion classification is designed,which combines prompt sentence vector classification with the template-verbalizer structure of prompt learning classification with multiple masks,introducing text,topic,and stance words information into sentence vectors,fusing sentence vectors and verbalizer classification results,and jointly optimizing the model.Experiments on the NLPCC Chinese stance detection dataset show that in the ex-periments of training separate models for five topics,the proposed method is superior or comparable to the previous best method in three targets,achieving a total F1 value of 79.3,which is close to the best method.The advantage of prompt sentence vectors is verified in the sentence vector comparison experiment.关键词
立场检测/深度学习/提示学习/句向量/多掩码Key words
stance detection/deep learning/prompt learning/sentence vector/multi-mask分类
信息技术与安全科学引用本文复制引用
王正佳,李霏,姬东鸿,滕冲..基于多掩码与提示句向量融合分类的立场检测[J].计算机技术与发展,2023,33(12):156-162,7.基金项目
国家自然科学基金(62176187) （62176187）
