Thank “Goodness”! A Way to Measure Style in Student Essays

Sandeep Mathias, Pushpak Bhattacharyya


Abstract
Essays have two major components for scoring - content and style. In this paper, we describe a property of the essay, called goodness, and use it to predict the score given for the style of student essays. We compare our approach to solve this problem with baseline approaches, like language modeling and also a state-of-the-art deep learning system. We show that, despite being quite intuitive, our approach is very powerful in predicting the style of the essays.
Anthology ID:
W18-3705
Volume:
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Yuen-Hsien Tseng, Hsin-Hsi Chen, Vincent Ng, Mamoru Komachi
Venue:
NLP-TEA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
35–41
Language:
URL:
https://aclanthology.org/W18-3705
DOI:
10.18653/v1/W18-3705
Bibkey:
Cite (ACL):
Sandeep Mathias and Pushpak Bhattacharyya. 2018. Thank “Goodness”! A Way to Measure Style in Student Essays. In Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications, pages 35–41, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Thank “Goodness”! A Way to Measure Style in Student Essays (Mathias & Bhattacharyya, NLP-TEA 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-3705.pdf