@inproceedings{ea2bb546d4844c87ac0168ec6c7e04af,
title = "Chinese Grammatical Error Detection Using Adversarial ELECTRA Transformers",
abstract = "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.",
keywords = "adversarial learning, Grammatical error diagnosis, neural networks, transformers",
author = "Lee, {Lung Hao} and Hung, {Man Chen} and Chen, {Chao Yi} and Chen, {Rou An} and Tseng, {Yuen Hsien}",
note = "Funding Information: This study was partially supported by the Ministry of Science and Technology, under the grant 106-2221-E-003-030-MY2, 108-2218-E-008-017-MY3 and 109-2410-H-003-123-MY3. Publisher Copyright: {\textcopyright} 2021 29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings. All rights reserved; 29th International Conference on Computers in Education Conference, ICCE 2021 ; Conference date: 22-11-2021 Through 26-11-2021",
year = "2021",
month = nov,
day = "22",
language = "English",
series = "29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings",
publisher = "Asia-Pacific Society for Computers in Education",
pages = "111--113",
editor = "Rodrigo, {Maria Mercedes T.} and Sridhar Iyer and Antonija Mitrovic and Cheng, {Hercy N. H.} and Dan Kohen-Vacs and Camillia Matuk and Agnieszka Palalas and Ramkumar Rajenran and Kazuhisa Seta and Jingyun Wang",
booktitle = "29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings",
}