Automatic Essay Scoring Method Based on Multi-Scale Features

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Applied sciences

سال: 2023

ISSN: ['2076-3417']

DOI: https://doi.org/10.3390/app13116775