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This paper describes our system at NLPTEA-2020 Task: Chinese GrammaticalError Diagnosis (CGED). The goal of CGED is to diagnose four types ofgrammatical errors: word selection (S), redundant words (R), missing words (M),and disordered words (W). The automatic CGED system contains two partsincluding error detection and error correction and our system is designed tosolve the error detection problem. Our system is built on three models: 1) aBERT-based model leveraging syntactic information; 2) a BERT-based modelleveraging contextual embeddings; 3) a lexicon-based graph neural networkleveraging lexical information. We also design an ensemble mechanism to improvethe single model's performance. Finally, our system achieves the highest F1scores at detection level and identification level among all teamsparticipating in the CGED 2020 task.

Combining GCN and Transformer for Chinese Grammatical Error Detection