DREsS: Dataset for Rubric-based Essay Scoring on EFL Writing
Haneul Yoo Jieun Han So-Yeon Ahn Alice Oh
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. DREsS comprises three sub-datasets: DREsS_New,
DREsS_Std., and DREsS_CASE. We collect DREsS_New, a real-classroom dataset with
1.7K 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 20K 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.