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.

DREsS 是一个大规模的基于评分标准的自动作文评分数据集，包含 DREsS_New、DREsS_Std 和 DREsS_CASE 三个子数据集。该研究还提出了一种基于损坏的增强策略 CASE，通过生成 20K 个合成样本来改善基线结果 45.44％。DREsS 将为进一步研究提供更准确、实用的 EFL 写作教育自动评分系统。