Chinese grammatical error detection using a CNN-LSTM model

Lung Hao Lee, Bo Lin Lin, Liang Chih Yu, Yuen Hsien Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings
EditorsAhmad Fauzi Mohd Ayub, Antonija Mitrovic, Jie-Chi Yang, Su Luan Wong, Wenli Chen
PublisherAsia-Pacific Society for Computers in Education
Pages919-921
Number of pages3
ISBN (Print)9789869401265
StatePublished - 2017
Event25th International Conference on Computers in Education, ICCE 2017 - Christchurch, New Zealand
Duration: 4 Dec 20178 Dec 2017

Publication series

NameProceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings

Conference

Conference25th International Conference on Computers in Education, ICCE 2017
Country/TerritoryNew Zealand
CityChristchurch
Period4/12/178/12/17

Keywords

  • Chinese as a foreign language
  • Deep neural networks
  • Grammatical error diagnosis

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