  This paper describes our system at NLPTEA-2020 Task: Chinese Grammatical
Error Diagnosis (CGED). The goal of CGED is to diagnose four types of
grammatical errors: word selection (S), redundant words (R), missing words (M),
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
leveraging lexical information. We also design an ensemble mechanism to improve
the single model's performance. Finally, our system achieves the highest F1
scores at detection level and identification level among all teams
participating in the CGED 2020 task.
