DREsS: Dataset for Rubric-based Essay Scoring on EFL Writing
<|reference_start|>DREsS: Dataset for Rubric-based Essay Scoring on EFL Writing: 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.<|reference_end|>
@article{yoo2024dress:,
    title={DREsS: Dataset for Rubric-based Essay Scoring on EFL Writing},
    author={Haneul Yoo, Jieun Han, So-Yeon Ahn, Alice Oh},
    journal={arXiv preprint arXiv:2402.16733},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16733},
    primaryClass={cs.CL cs.AI}
}