DREsS: Dataset for Rubric-based Essay Scoring on EFL Writing
Haneul Yoo, Jieun Han, So-Yeon Ahn, Alice Oh
Abstract
Automated essay scoring (AES) is a useful tool in English as a Foreign Language (EFL) writing education, offering real-time essay scores for students and instructors. However, previous AES models were trained on essays and scores irrelevant to the practical scenarios of EFL writing education and usually provided a single holistic score due to the lack of appropriate datasets. In this paper, we release DREsS, a large-scale, standard dataset for rubric-based automated essay scoring with 48.9K samples in total. DREsS comprises three sub-datasets: DREsS_New, DREsS_Std., and DREsS_CASE. We collect DREsS_New, a real-classroom dataset with 2.3K essays authored by EFL undergraduate students and scored by English education experts. We also standardize existing rubric-based essay scoring datasets as DREsS_Std. We suggest CASE, a corruption-based augmentation strategy for essays, which generates 40.1K synthetic samples of DREsS_CASE and improves the baseline results by 45.44%. DREsS will enable further research to provide a more accurate and practical AES system for EFL writing education.
BibTeX
@inproceedings{yoo-etal-2025-dress,
title = "{DRE}s{S}: Dataset for Rubric-based Essay Scoring on {EFL} Writing",
author = "Yoo, Haneul and
Han, Jieun and
Ahn, So-Yeon and
Oh, Alice",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.659/",
doi = "10.18653/v1/2025.acl-long.659",
pages = "13439--13454",
ISBN = "979-8-89176-251-0"
}