Multi-Channel CNN-BiLSTM for Chinese Grammatical Error Detection

Authors

  • Lung-Hao LEE Department of Electrical Engineering, National Central University, Taiwan Author
  • Yuh-Shyang WANG Department of Electrical Engineering, National Central University, Taiwan Author
  • Po-Chen LIN Department of Electrical Engineering, National Central University, Taiwan Author
  • Chih-Te HUNG Department of Electrical Engineering, National Central University, Taiwan Author
  • Yuen-Hsien TSENG Graduate Institute of Library and Information Studies, National Taiwan Normal University, Taiwan Author

Abstract

In this paper, we proposed a Multi-Channel Convolutional Neural Network with Bidirectional Long Short-Term Memory (MC-CNN-BiLSTM) model for Chinese grammatical error detection. The TOCFL learner corpus is adopted to measure the system capability of indicating whether a sentence contains errors or not. Our model performs better than a previous CNN-LSTM model that reflects the effectiveness of multi-channel embedding representation.

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Published

2020-11-23

Conference Proceedings Volume

2020: ICCE 2020: The 28th International Conference on Computers in Education

Section

Articles

How to Cite

Multi-Channel CNN-BiLSTM for Chinese Grammatical Error Detection. (2020). International Conference on Computers in Education, 558-560. https://library.apsce.net/index.php/ICCE/article/view/3975