Key points are not available for this paper at this time.
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 rubricbased 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. 11. DREsSNew (2, 279 samples) EFL classroom data: 1) Student-written essays 2) Rubric-based scores assessed by instructors 2. DREsSStd. (6, 515 samples)
Yoo et al. (Wed,) studied this question.