Essay Scoring Tool by Employing RoBERTa Architecture
Majdi Beseiso · 2021
The automated essay scoring (AES) has significant importance in machine grading of student essays particularly in standardized exams like the Graduate Record Examination (GRE). However, some issues in AES have remained unsolved over the past several years. The current approaches have scrutinized AES from both classification and regression perspectives. This study discusses the cutting edge architectures such as RoBERTa, XLNet, and BERT and compares their automated essay scoring performance. ASAP, a publicly accessible dataset is used for this purpose. The obtained results indicate that the natural language understanding (NLU) model proposed in this paper depicts significantly improved performance than all the other existing approaches.