DREsS: Dataset for Rubric-based Essay Scoring on EFL Writing
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.
Keywords
Cite
@article{arxiv.2402.16733,
title = {DREsS: Dataset for Rubric-based Essay Scoring on EFL Writing},
author = {Haneul Yoo and Jieun Han and So-Yeon Ahn and Alice Oh},
journal= {arXiv preprint arXiv:2402.16733},
year = {2025}
}
Comments
To appear in ACL 2025. arXiv admin note: text overlap with arXiv:2310.05191