mlsanityData to 7 Oct 2026

A Syntax-Guided Grammatical Error Correction Model with Dependency Tree Correction

arXiv 2111.03294 ↗First posted 5 Nov 2021cs.CLcited by 4

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Abstract (arXiv)

Grammatical Error Correction (GEC) is a task of detecting and correcting grammatical errors in sentences. Recently, neural machine translation systems have become popular approaches for this task. However, these methods lack the use of syntactic knowledge which plays an important role in the correction of grammatical errors. In this work, we propose a syntax-guided GEC model (SG-GEC) which adopts the graph attention mechanism to utilize the syntactic knowledge of dependency trees. Considering the dependency trees of the grammatically incorrect source sentences might provide incorrect syntactic knowledge, we propose a dependency tree correction task to deal with it. Combining with data augmentation method, our model achieves strong performances without using any large pre-trained models. We evaluate our model on public benchmarks of GEC task and it achieves competitive results.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 24 of its 43 references that are in the map. First posted on arXiv 5 Nov 2021.

Builds on

24 of 43 references.

References · 24

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12 more
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Cited by year

Citing papers per year: 2022: 2; 2023: 2.

4 papers.

  1. 2022SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parsercited by 32
  2. 2022CSynGEC: Incorporating Constituent-based Syntax for Grammatical Error Correction with a Tailored GEC-Oriented Parsercited by 4
  3. 2023Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmentingcited by 2
  4. 2023Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correctioncited by 0