This work proposes a syntax-enhanced grammatical error correction (GEC) approach named SynGEC that effectively incorporates dependency syntactic information into the encoder part of GEC models. The key challenge for this idea is that off-the-shelf parsers are unreliable when processing ungrammatical sentences. To confront this challenge, we propose to build a tailored GEC-oriented parser (GOPar) using parallel GEC training data as a pivot. First, we design an extended syntax representation scheme that allows us to represent both grammatical errors and syntax in a unified tree structure. Then, we obtain parse trees of the source incorrect sentences by projecting trees of the target correct sentences. Finally, we train GOPar with such projected trees. For GEC, we employ the graph convolution network to encode source-side syntactic information produced by GOPar, and fuse them with the outputs of the Transformer encoder. Experiments on mainstream English and Chinese GEC datasets show that our proposed SynGEC approach consistently and substantially outperforms strong baselines and achieves competitive performance. Our code and data are all publicly available at https://github.com/HillZhang1999/SynGEC.

该研究提出了一种名为SynGEC的句法增强语法纠错方法，它有效地将依赖句法信息纳入GEC模型的编码器部分，其中重点挑战是在处理不合语法的句子时，标准分析器是不可靠的。为了克服这一挑战，我们提出使用并行GEC训练数据作为枢轴构建定制的GEC定向解析器(GOPar)，并采用图卷积网络来编码GOPar产生的源侧句法信息，将它们与转换器编码器的输出融合，在英汉GEC数据集上的实验表明，我们提出的SynGEC方法一致且大幅优于强大的基准，并取得了有竞争力的性能

SynGEC: 带定制的 GEC 导向解析器的句法增强语法错误修正