SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser
This work addresses a domain-specific bottleneck in GEC by improving parsing accuracy for ungrammatical text, though it is incremental as it builds on existing GEC models.
The authors tackled the problem of unreliable syntactic parsing for ungrammatical sentences in grammatical error correction (GEC) by developing a tailored GEC-oriented parser (GOPar) and integrating it into a syntax-enhanced GEC model called SynGEC, which consistently outperformed strong baselines on English and Chinese datasets.
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.