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

作　　者：马钰 杨勇[1] 任鸽[1] 帕力旦·吐尔逊 MA Yu;YANG Yong;REN Ge;Palidan Tuerxun(School of Computer Science and Technology,Xinjiang Normal University,Urumqi 830054,China)

机构地区：[1]新疆师范大学计算机科学技术学院,新疆乌鲁木齐830054

出　　处：《计算机与现代化》2024年第9期33-37,44,共6页Computer and Modernization

基　　金：国家自然科学基金资助项目(62167008,62066044)。

摘　　要：作文自动评分是智慧教育领域的重要研究方向之一。它具有提高评分效率、降低人工成本以及确保评分客观性和一致性的优势,因此在教育领域有着广泛的应用前景。尽管句法特征在作文自动评分中扮演着重要角色,但目前仍缺乏关于如何更好地利用这些特征进行作文自动评分的研究。本文提出一种基于GCN和微调BERT的作文自动评分方法GFTB。该模型采用图卷积网络提取作文的句法特征,采用BERT和Adapter的训练方式提取作文的深层语义特征,同时采用门控机制进一步捕捉二者融合后的语义特征。实验结果表明,本文提出的GFTB模型在公共数据集ASAP的8个子集上取得了较好的平均性能,相比于通义千问等基线模型,能够有效提升作文自动评分的性能。Automatic scoring of essays is one of the important research directions in the field of smart education.It has the advantages 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 paper 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 experimental results show that the proposed GFTB model achieves good average performance on 8 subsets of the public data set ASAP.Compared 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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]