Automatic Essay Multi-dimensional Scoring with Fine-tuning and Multiple Regression

TL;DR

This paper presents two models for automated essay scoring that evaluate multiple writing dimensions simultaneously, using fine-tuning on large datasets, and outperform existing methods in accuracy and reliability.

Contribution

The paper introduces novel multi-dimensional scoring models for AES that provide detailed feedback across various writing aspects, improving upon single-score systems.

Findings

Models achieve high precision and F1 scores.

System outperforms existing AES methods.

Effective multi-dimensional scoring demonstrated.

Abstract

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.