Tzu-Mi Lin, Chao-Yi Chen, Lung-Hao Lee, and Yuen-Hsien Tseng.
In Proceedings of the 30th International Conference on Computers in Education (ICCE’22), pages 524-526.
Abstract
In this paper, we describe the process of building a benchmark data set for Chinese multi-label grammatical error detection tasks, comparing the performance of 10 representative neural network models. Experimental results reveal that no matter which deep learning model is used, the performance is still limited which confirms the difficulty of the multi-label detection task. Our constructed datasets and evaluation results will be publicly released on the GitHub repository (https://github.com/NCUEE-NLPLab/CMLGED) to promote further research to facilitate technology-enhanced Chinese learning.
