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Grammatical Error Correction in Low-Resource Scenarios

arXiv 1910.00353 ↗First posted 1 Oct 2019cs.CLcited by 28

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Abstract (arXiv)

Grammatical error correction in English is a long studied problem with many existing systems and datasets. However, there has been only a limited research on error correction of other languages. In this paper, we present a new dataset AKCES-GEC on grammatical error correction for Czech. We then make experiments on Czech, German and Russian and show that when utilizing synthetic parallel corpus, Transformer neural machine translation model can reach new state-of-the-art results on these datasets. AKCES-GEC is published under CC BY-NC-SA 4.0 license at https://hdl.handle.net/11234/1-3057 and the source code of the GEC model is available at https://github.com/ufal/low-resource-gec-wnut2019.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Oct 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 4; 2023: 9; 2024: 4; 2025: 2; 2026: 3.

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  1. 2021A Simple Recipe for Multilingual Grammatical Error Correctioncited by 108
  2. 2022Grammatical Error Correction: A Survey of the State of the Artcited by 43
  3. 2021LM-Critic: Language Models for Unsupervised Grammatical Error Correctioncited by 33
  4. 2023On the application of Large Language Models for language teaching and assessment technologycited by 27
  5. 2022Text Generation with Text-Editing Modelscited by 21
  6. 2021UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Languagecited by 19
  7. 2024GPT-3.5 for Grammatical Error Correctioncited by 18
  8. 2022Czech Grammar Error Correction with a Large and Diverse Corpuscited by 17
  9. 2022A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Modelcited by 16
  10. 2024BenCzechMark : A Czech-centric Multitask and Multimetric Benchmark for Large Language Models with Duel Scoring Mechanismcited by 12
  11. 2025Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectivescited by 10
  12. 2021Evaluating the Morphosyntactic Well-formedness of Generated Textscited by 8
16 more
  1. 2021Understanding Model Robustness to User-generated Noisy Textscited by 8
  2. 2023Non-autoregressive Text Editing with Copy-aware Latent Alignmentscited by 8
  3. 2021Character Transformations for Non-Autoregressive GEC Taggingcited by 7
  4. 2023Byte-Level Grammatical Error Correction Using Synthetic and Curated Corporacited by 7
  5. 2023CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correctioncited by 5
  6. 2023Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedulecited by 5
  7. 2023Beqi: Revitalize the Senegalese Wolof Language with a Robust Spelling Correctorcited by 4
  8. 2023GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decodingcited by 3
  9. 2024To Err Is Human, but Llamas Can Learn It Toocited by 3
  10. 2023Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmentingcited by 2
  11. 2026Different Time, Different Language: Revisiting the Bias Against Non-Native Speakers in GPT Detectorscited by 2
  12. 2026Neural Grammatical Error Correction for Romaniancited by 2
  13. 2024GECTurk WEB: An Explainable Online Platform for Turkish Grammatical Error Detection and Correctioncited by 1
  14. 2026Edit-level Majority Voting Mitigates Over-Correction in LLM-based Grammatical Error Correctioncited by 1
  15. 2023A Language Model for Grammatical Error Correction in L2 Russiancited by 0
  16. 2025Refining Czech GEC: Insights from a Multi-Experiment Approachcited by 0