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^*

^*Corresponding author for this work

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

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
Publication status	Published - 2022 Nov 28
Event	30th International Conference on Computers in Education Conference, ICCE 2022 - Kuala Lumpur, Malaysia Duration: 2022 Nov 28 → 2022 Dec 2

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	2022/11/28 → 2022/12/02

Keywords

Grammatical error detection
deep learning
multi-label classification

ASJC Scopus subject areas

Computer Science (miscellaneous)
Education

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.

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

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, 2022/11/28.

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).

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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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.",

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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