Domain-Specific Hybrid BERT based System for Automatic Short Answer Grading

  • Jai Garg
  • Jatin Papreja
  • Kumar Apurva
  • Dr. Goonjan Jain


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.

Keywords: Automatic Short Answer Grading (ASAG), Se- mantic Text Similarity, Key-Response Similarity, Bidirectional Encoder Representation from Transformers (BERT), Masked and Permuted Pre-training for Language Understanding (MPNet)


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How to Cite
Garg, J., Papreja, J., Apurva, K., & Jain, D. G. (2022). Domain-Specific Hybrid BERT based System for Automatic Short Answer Grading. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 8(2), 39-44.