Xiaoqing Lyu


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String Editing Based Chinese Grammatical Error Diagnosis
Haihua Xie | Xiaoqing Lyu | Xuefei Chen
Proceedings of the 29th International Conference on Computational Linguistics

Chinese Grammatical Error Diagnosis (CGED) suffers the problems of numerous types of grammatical errors and insufficiency of training data. In this paper, we propose a string editing based CGED model that requires less training data by using a unified workflow to handle various types of grammatical errors. Two measures are proposed in our model to enhance the performance of CGED. First, the detection and correction of grammatical errors are divided into different stages. In the stage of error detection, the model only outputs the types of grammatical errors so that the tag vocabulary size is significantly reduced compared with other string editing based models. Secondly, the correction of some grammatical errors is converted to the task of masked character inference, which has plenty of training data and mature solutions. Experiments on datasets of NLPTEA-CGED demonstrate that our model outperforms other CGED models in many aspects.


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基于数据增强和多任务特征学习的中文语法错误检测方法(Chinese Grammar Error Detection based on Data Enhancement and Multi-task Feature Learning)
Haihua Xie (谢海华) | Zhiyou Chen (陈志优) | Jing Cheng (程静) | Xiaoqing Lyu (吕肖庆) | Zhi Tang (汤帜)
Proceedings of the 19th Chinese National Conference on Computational Linguistics