Essay Quality Signals as Weak Supervision for Source-based Essay Scoring

Haoran Zhang, Diane Litman


Abstract
Human essay grading is a laborious task that can consume much time and effort. Automated Essay Scoring (AES) has thus been proposed as a fast and effective solution to the problem of grading student writing at scale. However, because AES typically uses supervised machine learning, a human-graded essay corpus is still required to train the AES model. Unfortunately, such a graded corpus often does not exist, so creating a corpus for machine learning can also be a laborious task. This paper presents an investigation of replacing the use of human-labeled essay grades when training an AES system with two automatically available but weaker signals of essay quality: word count and topic distribution similarity. Experiments using two source-based essay scoring (evidence score) corpora show that while weak supervision does not yield a competitive result when training a neural source-based AES model, it can be used to successfully extract Topical Components (TCs) from a source text, which are required by a supervised feature-based AES model. In particular, results show that feature-based AES performance is comparable with either automatically or manually constructed TCs.
Anthology ID:
2021.bea-1.9
Volume:
Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
Month:
April
Year:
2021
Address:
Online
Editors:
Jill Burstein, Andrea Horbach, Ekaterina Kochmar, Ronja Laarmann-Quante, Claudia Leacock, Nitin Madnani, Ildikó Pilán, Helen Yannakoudakis, Torsten Zesch
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
85–96
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2021.bea-1.9/
DOI:
Bibkey:
Cite (ACL):
Haoran Zhang and Diane Litman. 2021. Essay Quality Signals as Weak Supervision for Source-based Essay Scoring. In Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, pages 85–96, Online. Association for Computational Linguistics.
Cite (Informal):
Essay Quality Signals as Weak Supervision for Source-based Essay Scoring (Zhang & Litman, BEA 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2021.bea-1.9.pdf