每年專案
摘要
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.
原文 | ???core.languages.en_GB??? |
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主出版物標題 | 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings |
編輯 | 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 |
發行者 | Asia-Pacific Society for Computers in Education |
頁面 | 524-526 |
頁數 | 3 |
ISBN(電子) | 9789869721493 |
出版狀態 | 已出版 - 28 11月 2022 |
事件 | 30th International Conference on Computers in Education Conference, ICCE 2022 - Kuala Lumpur, Malaysia 持續時間: 28 11月 2022 → 2 12月 2022 |
出版系列
名字 | 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings |
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卷 | 1 |
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???event.eventtypes.event.conference??? | 30th International Conference on Computers in Education Conference, ICCE 2022 |
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國家/地區 | Malaysia |
城市 | Kuala Lumpur |
期間 | 28/11/22 → 2/12/22 |
指紋
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