# Grammatical Error Correction in Low-Resource Scenarios

arXiv: https://arxiv.org/abs/1910.00353 · HTML: https://mlsanity.com/paper/1910.00353/

## Key facts

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

## Built on by (most cited first, 28 of 28)

- [A Simple Recipe for Multilingual Grammatical Error Correction](https://mlsanity.com/paper/2106.03830/) (2021)
- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [LM-Critic: Language Models for Unsupervised Grammatical Error Correction](https://mlsanity.com/paper/2109.06822/) (2021)
- [On the application of Large Language Models for language teaching and assessment technology](https://mlsanity.com/paper/2307.08393/) (2023)
- [Text Generation with Text-Editing Models](https://mlsanity.com/paper/2206.07043/) (2022)
- [UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language](https://mlsanity.com/paper/2103.16997/) (2021)
- [GPT-3.5 for Grammatical Error Correction](https://mlsanity.com/paper/2405.08469/) (2024)
- [Czech Grammar Error Correction with a Large and Diverse Corpus](https://mlsanity.com/paper/2201.05590/) (2022)
- [A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model](https://mlsanity.com/paper/2201.10707/) (2022)
- [BenCzechMark : A Czech-centric Multitask and Multimetric Benchmark for Large Language Models with Duel Scoring Mechanism](https://mlsanity.com/paper/2412.17933/) (2024)
- [Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectives](https://mlsanity.com/paper/2508.02853/) (2025)
- [Evaluating the Morphosyntactic Well-formedness of Generated Texts](https://mlsanity.com/paper/2103.16590/) (2021)
- [Understanding Model Robustness to User-generated Noisy Texts](https://mlsanity.com/paper/2110.07428/) (2021)
- [Non-autoregressive Text Editing with Copy-aware Latent Alignments](https://mlsanity.com/paper/2310.07821/) (2023)
- [Character Transformations for Non-Autoregressive GEC Tagging](https://mlsanity.com/paper/2111.09280/) (2021)
- [Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora](https://mlsanity.com/paper/2305.17906/) (2023)
- [CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction](https://mlsanity.com/paper/2305.10819/) (2023)
- [Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule](https://mlsanity.com/paper/2311.11813/) (2023)
- [Beqi: Revitalize the Senegalese Wolof Language with a Robust Spelling Corrector](https://mlsanity.com/paper/2305.08518/) (2023)
- [GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decoding](https://mlsanity.com/paper/2311.08191/) (2023)
- [To Err Is Human, but Llamas Can Learn It Too](https://mlsanity.com/paper/2403.05493/) (2024)
- [Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting](https://mlsanity.com/paper/2310.13321/) (2023)
- [Different Time, Different Language: Revisiting the Bias Against Non-Native Speakers in GPT Detectors](https://mlsanity.com/paper/2602.05769/) (2026)
- [Neural Grammatical Error Correction for Romanian](https://mlsanity.com/paper/2604.23627/) (2026)
- [GECTurk WEB: An Explainable Online Platform for Turkish Grammatical Error Detection and Correction](https://mlsanity.com/paper/2410.12350/) (2024)
- [Edit-level Majority Voting Mitigates Over-Correction in LLM-based Grammatical Error Correction](https://mlsanity.com/paper/2605.13624/) (2026)
- [A Language Model for Grammatical Error Correction in L2 Russian](https://mlsanity.com/paper/2307.01609/) (2023)
- [Refining Czech GEC: Insights from a Multi-Experiment Approach](https://mlsanity.com/paper/2506.22402/) (2025)

## 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.