Combining GCN and Transformer for Chinese Grammatical Error Detection

• URL: http://arxiv.org/abs/2105.09085v1
• Date: Wed, 19 May 2021 12:17:07 GMT
• Title: Combining GCN and Transformer for Chinese Grammatical Error Detection
• Authors: Jinhong Zhang
• Abstract summary: This paper introduces our system at NLPTEA-2020 Task: Chinese Grammatical Error Diagnosis (CGED) CGED aims to diagnose four types of grammatical errors which are missing words (M), redundant words (R), bad word selection (S) and disordered words (W)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces our system at NLPTEA-2020 Task: Chinese Grammatical Error Diagnosis (CGED). CGED aims to diagnose four types of grammatical errors which are missing words (M), redundant words (R), bad word selection (S) and disordered words (W). The automatic CGED system contains two parts including error detection and error correction and our system is designed to solve the error detection problem. Our system is built on three models: 1) a BERT-based model leveraging syntactic information; 2) a BERT-based model leveraging contextual embeddings; 3) a lexicon-based graph neural network. We also design an ensemble mechanism to improve the performance of the single model. Finally, our system obtains the highest F1 scores at detection level and identification level among all teams participating in the CGED 2020 task.

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