计算机与现代化Issue(9)：33-37,44,6.DOI:10.3969/j.issn.1006-2475.2024.09.006
基于GCN和微调BERT的作文自动评分方法
Automated Essay Scoring Method Based on GCN and Fine Tuned BERT
摘要
Abstract
Automatic scoring of essays is one of the important research directions in the field of smart education.It has the advan-tages of improving scoring efficiency,reducing labor costs,and ensuring the objectivity and consistency of scoring,so it has broad application prospects in the field of education.Although syntactic features play an important role in automatic scoring of compositions,there is still a lack of research on how to better utilize these features for automatic scoring of compositions.This pa-per proposes an automatic essay scoring method GFTB based on GCN and fine-tuned BERT.This model uses graph convolutional network to extract syntactic features of compositions,uses BERT and Adapter training methods to extract deep semantic features of compositions,and uses a gating mechanism to further capture the semantic features after the fusion of the two.The experimen-tal results show that the proposed GFTB model achieves good average performance on 8 subsets of the public data set ASAP.Com-pared with baseline models such as Tongyi Qianwen,the proposed method can effectively improve the performance of automatic essay scoring.关键词
作文自动评分/图神经网络/微调BERT/特征融合Key words
automatic essays scoring/graph neural network/fine-tuning BERT/feature fusion分类
信息技术与安全科学引用本文复制引用
马钰,杨勇,任鸽,帕力旦·吐尔逊..基于GCN和微调BERT的作文自动评分方法[J].计算机与现代化,2024,(9):33-37,44,6.基金项目
国家自然科学基金资助项目(62167008,62066044) （62167008,62066044）
