Chinese Grammatical Error Detection Using Adversarial ELECTRA Transformers

Lung Hao Lee, Man Chen Hung, Chao Yi Chen, Rou An Chen, Yuen Hsien Tseng*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings
EditorsMaria Mercedes T. Rodrigo, Sridhar Iyer, Antonija Mitrovic, Hercy N. H. Cheng, Dan Kohen-Vacs, Camillia Matuk, Agnieszka Palalas, Ramkumar Rajenran, Kazuhisa Seta, Jingyun Wang
PublisherAsia-Pacific Society for Computers in Education
Pages111-113
Number of pages3
ISBN (Electronic)9789869721479
Publication statusPublished - 2021 Nov 22
Event29th International Conference on Computers in Education Conference, ICCE 2021 - Virtual, Online
Duration: 2021 Nov 222021 Nov 26

Publication series

Name29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings
Volume1

Conference

Conference29th International Conference on Computers in Education Conference, ICCE 2021
CityVirtual, Online
Period2021/11/222021/11/26

Keywords

  • Grammatical error diagnosis
  • adversarial learning
  • neural networks
  • transformers

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Education

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