ET-GNN: Ensemble Transformer-Based Graph Neural Networks for Holistic Automated Essay Scoring

Essay writing tasks are crucial for assessing students’ writing skills, but manual evaluation can be time-consuming and prone to inconsistencies. Automated Essay Scoring (AES) offers a solution by automatically evaluating essays, reducing the need for human intervention. This paper presents a hybrid method, called Ensemble Transformer-Based Graph Neural Networks (ET-GNN), which integrates Transformer-based models with Graph Convolutional Networks (GCNs) for holistic AES. Three Transformer models, DistilBERT, RoBERTa, and DeBERTaV3, were individually fine-tuned to generate contextual embeddings for each essay. The GCNs process these embeddings, effectively capturing relevant semantic information and inter-essay similarities. Additionally, ensemble methods are used to combine the DistilBERT-GCN, RoBERTa-GCN, and DeBERTaV3-GCN models employing averaging for regression tasks, majority voting for classification tasks, and a weighted ensemble method for both types of tasks. The proposed ET-GNN method enhances the performance and robustness of AES systems, achieving Quadratic Weighted Kappa (QWK) scores of 0.835 and 0.841 on the ASAP and AES 2.0 datasets, respectively. These results outperform other state-of-the-art models based on Transformer or GCNs architectures for the AES task.