A Multi-Task Automated Assessment System for Essay Scoring

Most existing automated assessment (AA) systems focus on holistic scoring, falling short in providing learners with comprehensive feedback. In this paper, we propose a Multi-Task Automated Assessment (MTAA) system that can output detailed scores along multiple dimen- sions of essay quality to provide instructional feedback. This system is built on multi-task learning and incorporates Orthogonality Constraints (OC) to learn distinct information from different tasks. To achieve better training convergence, we develop a training strategy, Dynamic Learning Rate Decay (DLRD), to adapt the learning rates for tasks based on their loss descending rates. The results show that our proposed system achieves state-of-the-art performance on two benchmark datasets: EL- LIPSE and ASAP++. Furthermore, we utilize ChatGPT to assess essays in both zero-shot and few-shot contexts using an ELLIPSE subset. The findings suggest that ChatGPT has not yet achieved a level of scoring consistency equivalent to our developed MTAA system and that of hu- man raters.