ESAS: Towards Practical and Explainable Short Answer Scoring (Student Abstract)

Authors

  • Palak Goenka Indian Institute of Technology, Roorkee
  • Mehak Piplani MIDAS Lab, IIITD
  • Ramit Sawhney Netaji Subhas Institute of Technology
  • Puneet Mathur University of Maryland, College Park
  • Rajiv Ratn Shah MIDAS Lab, IIITD

DOI:

https://doi.org/10.1609/aaai.v34i10.7170

Abstract

Motivated by the mandate to design and deploy a practical, real-world educational tool for grading, we extensively explore linguistic patterns for Short Answer Scoring (SAS) as well as authorship feedback. We approach the SAS task via a multipronged approach that employs linguistic context features for capturing domain-specific knowledge while emphasizing on domain agnostic grading and detailed feedback via an ensemble of explainable statistical models. Our methodology quantitatively supersedes multiple automatic short answer scoring systems.

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Published

2020-04-03

How to Cite

Goenka, P., Piplani, M., Sawhney, R., Mathur, P., & Shah, R. R. (2020). ESAS: Towards Practical and Explainable Short Answer Scoring (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13797-13798. https://doi.org/10.1609/aaai.v34i10.7170

Issue

Vol. 34 No. 10: Issue 10: AAAI-20 Student Tracks

Section

Student Abstract Track