Combining GCN and Transformer for Chinese Grammatical Error Detection

TL;DR

This paper presents a system combining BERT-based models and graph neural networks for detecting four types of Chinese grammatical errors, achieving top performance in the CGED 2020 task.

Contribution

The paper introduces a hybrid approach integrating syntactic, contextual, and lexical information using BERT and graph neural networks for Chinese grammatical error detection.

Findings

Achieved highest F1 scores in CGED 2020 task

Demonstrated effectiveness of ensemble mechanism

Validated the benefit of combining multiple models

Abstract

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.