Automatic Essay Multi-dimensional Scoring with Fine-tuning and Multiple Regression
This addresses the need for detailed feedback for users and L2 learners in real-world applications, though it is incremental as it builds on existing automated essay scoring systems.
The paper tackled the problem of automated essay scoring by developing models that provide multi-dimensional scores (e.g., vocabulary, grammar) for English essays, achieving impressive performance in precision, F1 score, and Quadratic Weighted Kappa, and outperforming existing methods in overall scoring.
Automated essay scoring (AES) involves predicting a score that reflects the writing quality of an essay. Most existing AES systems produce only a single overall score. However, users and L2 learners expect scores across different dimensions (e.g., vocabulary, grammar, coherence) for English essays in real-world applications. To address this need, we have developed two models that automatically score English essays across multiple dimensions by employing fine-tuning and other strategies on two large datasets. The results demonstrate that our systems achieve impressive performance in evaluation using three criteria: precision, F1 score, and Quadratic Weighted Kappa. Furthermore, our system outperforms existing methods in overall scoring.