@inproceedings{yupei-renfen-2021-prompt,
title = "A Prompt-independent and Interpretable Automated Essay Scoring Method for {C}hinese Second Language Writing",
author = "Yupei, Wang and
Renfen, Hu",
editor = "Li, Sheng and
Sun, Maosong and
Liu, Yang and
Wu, Hua and
Liu, Kang and
Che, Wanxiang and
He, Shizhu and
Rao, Gaoqi",
booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
month = aug,
year = "2021",
address = "Huhhot, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2021.ccl-1.107",
pages = "1202--1217",
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.",
language = "English",
}
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%0 Conference Proceedings
%T A Prompt-independent and Interpretable Automated Essay Scoring Method for Chinese Second Language Writing
%A Yupei, Wang
%A Renfen, Hu
%Y Li, Sheng
%Y Sun, Maosong
%Y Liu, Yang
%Y Wu, Hua
%Y Liu, Kang
%Y Che, Wanxiang
%Y He, Shizhu
%Y Rao, Gaoqi
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 August
%I Chinese Information Processing Society of China
%C Huhhot, China
%G English
%F yupei-renfen-2021-prompt
%X 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.
%U https://aclanthology.org/2021.ccl-1.107
%P 1202-1217
Markdown (Informal)
[A Prompt-independent and Interpretable Automated Essay Scoring Method for Chinese Second Language Writing](https://aclanthology.org/2021.ccl-1.107) (Yupei & Renfen, CCL 2021)
ACL