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 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.