Data Augmentation by Rubrics for Short Answer Grading
Article Status
Published
Authors/contributors
- Wang, Tianqi (Author)
- Funayama, Hiroaki (Author)
- Ouchi, Hiroki (Author)
- Inui, Kentaro (Author)
Title
Data Augmentation by Rubrics for Short Answer Grading
Publication
Journal of Natural Language Processing
Date
2021
Volume
28
Issue
1
Pages
183-205
Journal Abbr
Journal of Natural Language Processing
Citation Key
wang2021b
Accessed
30/03/2023, 17:02
ISSN
1340-7619
Language
en
Library Catalogue
DOI.org (Crossref)
Extra
<标题>: 通过评分量表进行简答题评分的数据增强
<AI Smry>: This paper proposes a semi-supervised method to train a neural SAG model that extracts keyphrases that are highly related to answers scores from rubrics, and demonstrates that both performance of grading and justification identi-cation is improved by integrating attention semi- supervised training, especially in a low-resource setting.
Read_Status: New
Read_Status_Date: 2026-01-26T11:33:57.130Z
Citation
Wang, T., Funayama, H., Ouchi, H., & Inui, K. (2021). Data Augmentation by Rubrics for Short Answer Grading. Journal of Natural Language Processing, 28(1), 183–205. https://doi.org/10.5715/jnlp.28.183
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