A Prompt-independent and Interpretable Automated Essay Scoring Method for Chinese Second Language Writing

Wang Yupei, Hu Renfen


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
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:
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2021.ccl-1.107.pdf
Code
 iris2hu/l2c-rater