基于多层次语义特征的英文作文自动评分方法

作文自动评分（AES）技术能够自动地对作文进行分析和评分，其已成为自然语言处理技术在教育领域应用的热点研究问题之一。针对目前AES方法割裂了深层和浅层语义特征，忽视了多层次语义融合对作文评分影响的问题，提出了一种基于多层次语义特征的神经网络（MLSF）模型进行AES。首先，采用卷积神经网络（CNN）捕获局部语义特征，并采用混合神经网络捕获全局语义特征，以从深层次获取作文的语义特征；其次，利用篇章级的作文主题向量来获取主题层特征，同时针对深度学习模型难以挖掘的语法错误和语言丰富程度特征，构造了少量人工特征以从浅层获取作文的语言学特征；最后，通过特征融合对作文进行自动评分。实验结果表明，所提出模型在Kaggle ASAP竞赛公开数据集的所有子集上性能均有显著提升，该模型的平均二次加权的卡帕值（QWK）达到79.17%，验证了该模型在AES任务中的有效性。

Automated English essay scoring method based on multi-level semantic features

The Automated Essay Scoring (AES) technology can automatically analyze and score the essay, and has become one of the hot research problems in the application of natural language processing technology in the education field. Aiming at the current AES methods that separate deep and shallow semantic features, and ignore the impact of multi-level semantic fusion on essay scoring, a neural network model based on Multi-Level Semantic Features (MLSF) was proposed for AES. Firstly, Convolutional Neural Network (CNN) was used to capture local semantic features, and the hybrid neural network was used to capture global semantic features, so that the essay semantic features were obtained from a deep level. Secondly, the feature of the topic layer was obtained by using the essay topic vector of text level. At the same time, aiming at the grammatical errors and language richness features that are difficult to mine by deep learning model, a small number of artificial features were constructed to obtain the linguistic features of the essay from the shallow level. Finally, the essay was automatically scored through the feature fusion. Experimental results show that the proposed model improves the performance significantly on all subsets of the public dataset of the Kaggle ASAP (Automated Student Assessment Prize) champion, with the average Quadratic Weighted Kappa (QWK) of 79.17%, validating the effectiveness of the model in the AES tasks.