Automatic Essay Scoring(AES)is an important research topic for the application of Natural Language Processing(NLP)technology in the field of education.AES aims to improve scoring efficiency and enhance the objectivity and reliability of evaluations.This study proposes a topic perception and semantic enhancement approach for AES,addressing the issues of missing thematic relevance and loss of information in long texts,as well as leveraging the different levels of feature extraction capability in the pre-training language model,Bidirectional Encoder Representations from Transformers(BERT).This approach utilizes a multi-head attention mechanism to extract shallow semantic features of an essay and perceive its thematic characteristics.Additionally,it leverages the mid-level syntactic and deep semantic features of BERT to enhance the understanding of the semantics of the essay.Finally,the fused features from different dimensions are used for the AES.Experimental results indicate that the proposed model exhibits significant performance advantages for eight subsets of the ASAP public dataset.The proposed model effectively improves the performance of AES compared to that of baseline models,such as Qwen-7B;its average Quadratic Weighted Kappa(QWK)is 80.25％.
关键词
作文自动评分/语义增强/主题感知/特征融合/预训练语言模型
Key words
Automatic Essay Scoring(AES)/semantic enhancement/topic perception/feature fusion/pre-training language model
维普
万方数据