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

• URL: http://arxiv.org/abs/2210.12484v1
• Date: Sat, 22 Oct 2022 15:54:29 GMT
• Title: SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser
• Authors: Yue Zhang and Bo Zhang and Zhenghua Li and Zuyi Bao and Chen Li and Min Zhang
• Abstract summary: This work proposes a syntax-enhanced grammatical error correction (GEC) approach named SynGEC. To confront this challenge, we propose to build a tailored GEC-oriented (GOPar) using parallel GEC training data as a pivot. Experiments on mainstream English and Chinese GEC datasets show that our proposed SynGEC approach consistently and substantially outperforms strong baselines and achieves competitive performance.
• Score: 28.337533657684563
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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