LAUSR

Essays are a pivotal component of conventional exams; accurately, efficiently, and effectively grading them is a significant challenge for educators. Automated essay scoring (AES) is a complex task that utilizes computer technology to assist teachers in scoring. Traditional AES techniques only focus on shallow linguistic features based on the grading criteria, ignoring the influence of deep semantic features. The AES model based on deep neural networks (DNN) can eliminate the need for feature engineering and achieve better accuracy. In addition, the DNN-AES model combining different scales of essays has recently achieved excellent results. However, it has the following problems: (1) It mainly extracts sentence-scale features manually and cannot be fine-tuned for specific tasks. (2) It does not consider the shallow linguistic features that the DNN-AES cannot extract. (3) It does not contain the relevance between the essay and the corresponding prompt. To solve these problems, we propose an AES method based on multi-scale features. Specifically, we utilize Sentence-BERT (SBERT) to vectorize sentences and connect them to the DNN-AES model. Furthermore, the typical shallow linguistic features and prompt-related features are integrated into the distributed features of the essay. The experimental results show that the Quadratic Weighted Kappa of our proposed method on the Kaggle ASAP competition dataset reaches 79.3%, verifying the efficacy of the extended method in the AES task.