DeBERTa with hats makes Automated Essay Scoring system better

Automated Essay Scoring (AES) is a rapidly growing field that applies natural language processing (NLP) and machine learning techniques to the analysis and evaluation of academic essays. By automating the process of evaluating essay quality, AES not only greatly reduces the workload of human graders but also ensures consistency and objectivity in the evaluation process. AES systems can evaluate essays based on multiple criteria, including organization, coherence, and content. With the advent of deep learning, AES has shown significant improvements in accuracy and reliability. AES systems have numerous applications in education, particularly in large-scale assessment and feedback loops. In this article, we delve into the use of an improved Bidirectional Encoder Representations from Transformers (BERT) architecture with disentangled attention mechanism known as DeBERTa for student question-based summarization. This is one of the downstream tasks within AES, which is of great significance for student learning assessment. The organic combination of DeBERTa-v3 and diverse hats like Light Gradient Boosting Machine (LGBM) algorithm and Extreme Gradient Boosting algorithm (XGBoost) has proven to be highly effective in achieving excellent results in this task, indicating their significant potential in real-world AES systems.