@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://preview.aclanthology.org/ingest-emnlp/2025.acl-long.659/",
    doi = "10.18653/v1/2025.acl-long.659",
    pages = "13439--13454",
    ISBN = "979-8-89176-251-0",
    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."
}