ACL 2023findings11 citations
Score It All Together: A Multi-Task Learning Study on Automatic Scoring of Argumentative Essays
Yuning Ding, Marie Bexte, Andrea Horbach
Abstract
When scoring argumentative essays in an educational context, not only the presence or absence of certain argumentative elements but also their quality is important. On the recently published student essay dataset PERSUADE, we first show that the automatic scoring of argument quality benefits from additional information about context, writing prompt and argument type. We then explore the different combinations of three tasks: automated span detection, type and quality prediction. Results show that a multi-task learning approach combining the three tasks outperforms sequential approaches that first learn to segment and then predict the quality/type of a segment.
BibTeX
@inproceedings{ding-etal-2023-score,
title = "Score It All Together: A Multi-Task Learning Study on Automatic Scoring of Argumentative Essays",
author = "Ding, Yuning and
Bexte, Marie and
Horbach, Andrea",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.825/",
doi = "10.18653/v1/2023.findings-acl.825",
pages = "13052--13063"
}