Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning

Yucheng Wang, Zhongyu Wei, Yaqian Zhou, Xuanjing Huang


Abstract
Automatic essay scoring (AES) is the task of assigning grades to essays without human interference. Existing systems for AES are typically trained to predict the score of each single essay at a time without considering the rating schema. In order to address this issue, we propose a reinforcement learning framework for essay scoring that incorporates quadratic weighted kappa as guidance to optimize the scoring system. Experiment results on benchmark datasets show the effectiveness of our framework.
Anthology ID:
D18-1090
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
791–797
Language:
URL:
https://aclanthology.org/D18-1090
DOI:
10.18653/v1/D18-1090
Bibkey:
Cite (ACL):
Yucheng Wang, Zhongyu Wei, Yaqian Zhou, and Xuanjing Huang. 2018. Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 791–797, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning (Wang et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1090.pdf