Evaluating the Performance of Chinese Multi-Label Grammatical Error Detection Using Deep Neural Networks

Tzu Mi Lin; Chao Yi Chen; Lung Hao Lee; Yuen Hsien Tseng

Evaluating the Performance of Chinese Multi-Label Grammatical Error Detection Using Deep Neural Networks

Tzu Mi Lin, Chao Yi Chen, Lung Hao Lee, Yuen Hsien Tseng

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, we describe the process of building a benchmark data set for Chinese multi-label grammatical error detection tasks, comparing the performance of 10 representative neural network models. Experimental results reveal that no matter which deep learning model is used, the performance is still limited which confirms the difficulty of the multi-label detection task. Our constructed datasets and evaluation results will be publicly released on the GitHub repository (https://github.com/NCUEE-NLPLab/CMLGED) to promote further research to facilitate technology-enhanced Chinese learning.

Original language	English
Title of host publication	30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings
Editors	Sridhar Iyer, Ju-Ling Shih, Ju-Ling Shih, Weiqin Chen, Weiqin Chen, Mas Nida MD Khambari, Mouna Denden, Rwitajit Majumbar, Liliana Cuesta Medina, Shitanshu Mishra, Sahana Murthy, Patcharin Panjaburee, Daner Sun
Publisher	Asia-Pacific Society for Computers in Education
Pages	524-526
Number of pages	3
ISBN (Electronic)	9789869721493
State	Published - 28 Nov 2022
Event	30th International Conference on Computers in Education Conference, ICCE 2022 - Kuala Lumpur, Malaysia Duration: 28 Nov 2022 → 2 Dec 2022

Publication series

Name	30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings
Volume	1

Conference

Conference	30th International Conference on Computers in Education Conference, ICCE 2022
Country/Territory	Malaysia
City	Kuala Lumpur
Period	28/11/22 → 2/12/22

Keywords

Grammatical error detection
deep learning
multi-label classification

Chinese Knowledge Base Construction and Applications for Medical Healthcare Domain(3/3)
Lee, L.-H. (PI)
1/05/21 → 31/07/22
Project: Research

Cite this

Lin, T. M., Chen, C. Y., Lee, L. H., & Tseng, Y. H. (2022). Evaluating the Performance of Chinese Multi-Label Grammatical Error Detection Using Deep Neural Networks. In S. Iyer, J.-L. Shih, J.-L. Shih, W. Chen, W. Chen, M. N. MD. Khambari, M. Denden, R. Majumbar, L. C. Medina, S. Mishra, S. Murthy, P. Panjaburee, & D. Sun (Eds.), 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings (pp. 524-526). (30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings; Vol. 1). Asia-Pacific Society for Computers in Education.

Lin, Tzu Mi ; Chen, Chao Yi ; Lee, Lung Hao et al. / Evaluating the Performance of Chinese Multi-Label Grammatical Error Detection Using Deep Neural Networks. 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings. editor / Sridhar Iyer ; Ju-Ling Shih ; Ju-Ling Shih ; Weiqin Chen ; Weiqin Chen ; Mas Nida MD Khambari ; Mouna Denden ; Rwitajit Majumbar ; Liliana Cuesta Medina ; Shitanshu Mishra ; Sahana Murthy ; Patcharin Panjaburee ; Daner Sun. Asia-Pacific Society for Computers in Education, 2022. pp. 524-526 (30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings).

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title = "Evaluating the Performance of Chinese Multi-Label Grammatical Error Detection Using Deep Neural Networks",

abstract = "In this paper, we describe the process of building a benchmark data set for Chinese multi-label grammatical error detection tasks, comparing the performance of 10 representative neural network models. Experimental results reveal that no matter which deep learning model is used, the performance is still limited which confirms the difficulty of the multi-label detection task. Our constructed datasets and evaluation results will be publicly released on the GitHub repository (https://github.com/NCUEE-NLPLab/CMLGED) to promote further research to facilitate technology-enhanced Chinese learning.",

keywords = "Grammatical error detection, deep learning, multi-label classification",

author = "Lin, {Tzu Mi} and Chen, {Chao Yi} and Lee, {Lung Hao} and Tseng, {Yuen Hsien}",

note = "Publisher Copyright: {\textcopyright} 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings.; 30th International Conference on Computers in Education Conference, ICCE 2022 ; Conference date: 28-11-2022 Through 02-12-2022",

year = "2022",

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series = "30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings",

publisher = "Asia-Pacific Society for Computers in Education",

pages = "524--526",

editor = "Sridhar Iyer and Ju-Ling Shih and Ju-Ling Shih and Weiqin Chen and Weiqin Chen and Khambari, {Mas Nida MD} and Mouna Denden and Rwitajit Majumbar and Medina, {Liliana Cuesta} and Shitanshu Mishra and Sahana Murthy and Patcharin Panjaburee and Daner Sun",

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Lin, TM, Chen, CY, Lee, LH & Tseng, YH 2022, Evaluating the Performance of Chinese Multi-Label Grammatical Error Detection Using Deep Neural Networks. in S Iyer, J-L Shih, J-L Shih, W Chen, W Chen, MNMD Khambari, M Denden, R Majumbar, LC Medina, S Mishra, S Murthy, P Panjaburee & D Sun (eds), 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings. 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings, vol. 1, Asia-Pacific Society for Computers in Education, pp. 524-526, 30th International Conference on Computers in Education Conference, ICCE 2022, Kuala Lumpur, Malaysia, 28/11/22.

Evaluating the Performance of Chinese Multi-Label Grammatical Error Detection Using Deep Neural Networks. / Lin, Tzu Mi; Chen, Chao Yi; Lee, Lung Hao et al.
30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings. ed. / Sridhar Iyer; Ju-Ling Shih; Ju-Ling Shih; Weiqin Chen; Weiqin Chen; Mas Nida MD Khambari; Mouna Denden; Rwitajit Majumbar; Liliana Cuesta Medina; Shitanshu Mishra; Sahana Murthy; Patcharin Panjaburee; Daner Sun. Asia-Pacific Society for Computers in Education, 2022. p. 524-526 (30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Evaluating the Performance of Chinese Multi-Label Grammatical Error Detection Using Deep Neural Networks

AU - Lin, Tzu Mi

AU - Chen, Chao Yi

AU - Lee, Lung Hao

AU - Tseng, Yuen Hsien

N1 - Publisher Copyright: © 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings.

PY - 2022/11/28

Y1 - 2022/11/28

N2 - In this paper, we describe the process of building a benchmark data set for Chinese multi-label grammatical error detection tasks, comparing the performance of 10 representative neural network models. Experimental results reveal that no matter which deep learning model is used, the performance is still limited which confirms the difficulty of the multi-label detection task. Our constructed datasets and evaluation results will be publicly released on the GitHub repository (https://github.com/NCUEE-NLPLab/CMLGED) to promote further research to facilitate technology-enhanced Chinese learning.

AB - In this paper, we describe the process of building a benchmark data set for Chinese multi-label grammatical error detection tasks, comparing the performance of 10 representative neural network models. Experimental results reveal that no matter which deep learning model is used, the performance is still limited which confirms the difficulty of the multi-label detection task. Our constructed datasets and evaluation results will be publicly released on the GitHub repository (https://github.com/NCUEE-NLPLab/CMLGED) to promote further research to facilitate technology-enhanced Chinese learning.

KW - Grammatical error detection

KW - deep learning

KW - multi-label classification

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M3 - 會議論文篇章

AN - SCOPUS:85151054177

T3 - 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings

SP - 524

EP - 526

BT - 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings

A2 - Iyer, Sridhar

A2 - Shih, Ju-Ling

A2 - Chen, Weiqin

A2 - Khambari, Mas Nida MD

A2 - Denden, Mouna

A2 - Majumbar, Rwitajit

A2 - Medina, Liliana Cuesta

A2 - Mishra, Shitanshu

A2 - Murthy, Sahana

A2 - Panjaburee, Patcharin

A2 - Sun, Daner

PB - Asia-Pacific Society for Computers in Education

T2 - 30th International Conference on Computers in Education Conference, ICCE 2022

Y2 - 28 November 2022 through 2 December 2022

ER -

Lin TM, Chen CY, Lee LH, Tseng YH. Evaluating the Performance of Chinese Multi-Label Grammatical Error Detection Using Deep Neural Networks. In Iyer S, Shih JL, Shih JL, Chen W, Chen W, Khambari MNMD, Denden M, Majumbar R, Medina LC, Mishra S, Murthy S, Panjaburee P, Sun D, editors, 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings. Asia-Pacific Society for Computers in Education. 2022. p. 524-526. (30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings).

Evaluating the Performance of Chinese Multi-Label Grammatical Error Detection Using Deep Neural Networks

Abstract

Publication series

Conference

Keywords

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Projects

Chinese Knowledge Base Construction and Applications for Medical Healthcare Domain(3/3)

Cite this