@inproceedings{565e1a1139924c84b801e788e6af79b9,
title = "Chinese grammatical error detection using a CNN-LSTM model",
abstract = "In this paper, we proposed a Convolution Neural Network with Long Short-Term Memory (CNN-LSTM) model for Chinese grammatical error detection. The TOCFL learner corpus is adopted to measure the system performance of indicating whether a sentence contains errors or not. Our model performs better than other neural network based methods in terms of accuracy for identifying an erroneous sentence written by Chinese language learners.",
keywords = "Chinese as a foreign language, Deep neural networks, Grammatical error diagnosis",
author = "Lee, {Lung Hao} and Lin, {Bo Lin} and Yu, {Liang Chih} and Tseng, {Yuen Hsien}",
note = "Funding Information: This study was partially supported by the Ministry of Science and Technology, under the grant MOST 103-2221-E-003-013-MY3, MOST 105-2221-E-155-059-MY2, MOST 106-2221-E-003-030-MY2 and the “Aim for the Top University Project” and “Center of Language Technology for Chinese” of National Taiwan Normal University, sponsored by the Ministry of Education, Taiwan, ROC. Publisher Copyright: {\textcopyright} 2017 Asia-Pacific Society for Computers in Education. All rights reserved.; 25th International Conference on Computers in Education, ICCE 2017 ; Conference date: 04-12-2017 Through 08-12-2017",
year = "2017",
language = "English",
isbn = "9789869401265",
series = "Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings",
publisher = "Asia-Pacific Society for Computers in Education",
pages = "919--921",
editor = "{Mohd Ayub}, {Ahmad Fauzi} and Antonija Mitrovic and Jie-Chi Yang and Wong, {Su Luan} and Wenli Chen",
booktitle = "Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings",
}