# A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction

arXiv: https://arxiv.org/abs/2010.03155 · HTML: https://mlsanity.com/paper/2010.03155/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [Aligner: Efficient Alignment by Learning to Correct](https://mlsanity.com/paper/2402.02416/) (2024)
- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser](https://mlsanity.com/paper/2210.12484/) (2022)
- [The Better Angels of Machine Personality: How Personality Relates to LLM Safety](https://mlsanity.com/paper/2407.12344/) (2024)
- [Do Grammatical Error Correction Models Realize Grammatical Generalization?](https://mlsanity.com/paper/2106.03031/) (2021)
- [Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule](https://mlsanity.com/paper/2311.11813/) (2023)
- [SDA: Steering-Driven Distribution Alignment for Open LLMs without Fine-Tuning](https://mlsanity.com/paper/2511.16324/) (2025)
- [Detection-Correction Structure via General Language Model for Grammatical Error Correction](https://mlsanity.com/paper/2405.17804/) (2024)

## Abstract (arXiv)

Existing approaches for grammatical error correction (GEC) largely rely on supervised learning with manually created GEC datasets. However, there has been little focus on verifying and ensuring the quality of the datasets, and on how lower-quality data might affect GEC performance. We indeed found that there is a non-negligible amount of "noise" where errors were inappropriately edited or left uncorrected. To address this, we designed a self-refinement method where the key idea is to denoise these datasets by leveraging the prediction consistency of existing models, and outperformed strong denoising baseline methods. We further applied task-specific techniques and achieved state-of-the-art performance on the CoNLL-2014, JFLEG, and BEA-2019 benchmarks. We then analyzed the effect of the proposed denoising method, and found that our approach leads to improved coverage of corrections and facilitated fluency edits which are reflected in higher recall and overall performance.