Abstract
With the increasing popularity of learning Chinese as a second language (L2) the development of an automatic essay scoring (AES) method specially for Chinese L2 essays has become animportant task. To build a robust model that could easily adapt to prompt changes we propose 90linguistic features with consideration of both language complexity and correctness and introducethe Ordinal Logistic Regression model that explicitly combines these linguistic features and low-level textual representations. Our model obtains a high QWK of 0.714 a low RMSE of 1.516 anda considerable Pearson correlation of 0.734. With a simple linear model we further analyze the contribution of the linguistic features to score prediction revealing the model’s interpretability and its potential to give writing feedback to users. This work provides insights and establishes asolid baseline for Chinese L2 AES studies.- Anthology ID:
- 2021.ccl-1.107
- Original:
- 2021.ccl-1.107v1
- Version 2:
- 2021.ccl-1.107v2
- Volume:
- Proceedings of the 20th Chinese National Conference on Computational Linguistics
- Month:
- August
- Year:
- 2021
- Address:
- Huhhot, China
- Editors:
- Sheng Li (李生), Maosong Sun (孙茂松), Yang Liu (刘洋), Hua Wu (吴华), Kang Liu (刘康), Wanxiang Che (车万翔), Shizhu He (何世柱), Gaoqi Rao (饶高琦)
- Venue:
- CCL
- SIG:
- Publisher:
- Chinese Information Processing Society of China
- Note:
- Pages:
- 1202–1217
- Language:
- English
- URL:
- https://aclanthology.org/2021.ccl-1.107
- DOI:
- Cite (ACL):
- Wang Yupei and Hu Renfen. 2021. A Prompt-independent and Interpretable Automated Essay Scoring Method for Chinese Second Language Writing. In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 1202–1217, Huhhot, China. Chinese Information Processing Society of China.
- Cite (Informal):
- A Prompt-independent and Interpretable Automated Essay Scoring Method for Chinese Second Language Writing (Yupei & Renfen, CCL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.ccl-1.107.pdf
- Code
- iris2hu/l2c-rater