Domain-Specific Hybrid BERT based System for Automatic Short Answer Grading
Jai Garg, Jatin Papreja, Kumar Apurva, Goonjan Jain
Abstract
Effective and efficient grading has been recognized as an important issue in any educational institution. In this study, a grading system involving BERT for Automatic Short Answer Grading (ASAG) is proposed. A BERT Regressor model is fine-tuned using a domain-specific ASAG dataset to achieve a baseline performance. In order to improve the final grading performance, an effective strategy is proposed involving careful integration of BERT Regressor model with Semantic Text Similarity. A set of experiments is conducted to test the performance of the proposed method. Two performance metrics namely: Pearson's Correlation Coefficient and Root Mean Squared Error are used for evaluation purposes. The results obtained highlights the usefulness of proposed system for domain specific ASAG tasks in real life.