每年專案
摘要
We explore transformer-based neural networks for Chinese grammatical error detection. The TOCFL learner corpus is used to measure the model capability of indicating whether a sentence contains errors or not. Experimental results show that ELECTRA transformers which take into account both transformer architecture and adversarial learning technique can achieve promising effectiveness with an improvement of F1-score.
原文 | ???core.languages.en_GB??? |
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主出版物標題 | 29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings |
編輯 | Maria Mercedes T. Rodrigo, Sridhar Iyer, Antonija Mitrovic, Hercy N. H. Cheng, Dan Kohen-Vacs, Camillia Matuk, Agnieszka Palalas, Ramkumar Rajenran, Kazuhisa Seta, Jingyun Wang |
發行者 | Asia-Pacific Society for Computers in Education |
頁面 | 111-113 |
頁數 | 3 |
ISBN(電子) | 9789869721479 |
出版狀態 | 已出版 - 22 11月 2021 |
事件 | 29th International Conference on Computers in Education Conference, ICCE 2021 - Virtual, Online 持續時間: 22 11月 2021 → 26 11月 2021 |
出版系列
名字 | 29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings |
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卷 | 1 |
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???event.eventtypes.event.conference??? | 29th International Conference on Computers in Education Conference, ICCE 2021 |
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城市 | Virtual, Online |
期間 | 22/11/21 → 26/11/21 |
指紋
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