Papers › SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser

SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser

22 Oct 2022arXiv:2210.12484archive 2025-07-28

Yue Zhang, Bo Zhang, Zhenghua Li, Zuyi Bao, Chen Li, Min Zhang

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.

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Code

hillzhang1999/syngec officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Grammatical Error Correction Syntax Representation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Grammatical Error Correction	CoNLL-2014 Shared Task	SynGEC	F0.5	67.6	#9 of 23	Archive leaderboard	report
Grammatical Error Correction	CoNLL-2014 Shared Task	SynGEC	Precision	74.7	#9 of 23	Archive leaderboard	report
Grammatical Error Correction	CoNLL-2014 Shared Task	SynGEC	Recall	49.0	#9 of 23	Archive leaderboard	report

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Methods

Absolute Position Encodings Adam Attention BPE Convolution Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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