Abstract
Descriptive or essay type of answers have always played a major role in education. They clearly capture the student’s grasp on knowledge and presentation skills. Manual essay scoring can be a daunting process to human evaluators; assessing descriptive answers can present a huge overhead owing to limited numbers of evaluators and an out of proportional number of essays to be graded hence leading to an inefficient or an inaccurate score. There has been a major shift in paradigm from traditional classroom education to online education engendered by COVID-19 pandemic; it seems plausible to infer that future assessment of education shall be online, making the solution of automatic essay scorer not only relevant, but of paramount importance. We explore several neural architecture models for the task of automated essay scoring system. Results and Experimental analysis exhibit that our model based on recurrent encoder-decoder provides for a deeper semantic analysis hence, outperforming a strong baseline in terms of quadratic weighted kappa score.- Anthology ID:
- 2021.icon-main.48
- Volume:
- Proceedings of the 18th International Conference on Natural Language Processing (ICON)
- Month:
- December
- Year:
- 2021
- Address:
- National Institute of Technology Silchar, Silchar, India
- Editors:
- Sivaji Bandyopadhyay, Sobha Lalitha Devi, Pushpak Bhattacharyya
- Venue:
- ICON
- SIG:
- Publisher:
- NLP Association of India (NLPAI)
- Note:
- Pages:
- 399–407
- Language:
- URL:
- https://aclanthology.org/2021.icon-main.48
- DOI:
- Cite (ACL):
- Priyatam Naravajhula, Sreedeep Rayavarapu, and Srujana Inturi. 2021. Encoder Decoder Approach to Automated Essay Scoring For Deeper Semantic Analysis. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 399–407, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).
- Cite (Informal):
- Encoder Decoder Approach to Automated Essay Scoring For Deeper Semantic Analysis (Naravajhula et al., ICON 2021)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2021.icon-main.48.pdf