# Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task

arXiv: https://arxiv.org/abs/1804.05940 · HTML: https://mlsanity.com/paper/1804.05940/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2018.

## Built on by (most cited first, 30 of 56)

- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models](https://mlsanity.com/paper/2105.13318/) (2021)
- [Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods](https://mlsanity.com/paper/2305.18156/) (2023)
- [LM-Critic: Language Models for Unsupervised Grammatical Error Correction](https://mlsanity.com/paper/2109.06822/) (2021)
- [MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2204.10994/) (2022)
- [SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser](https://mlsanity.com/paper/2210.12484/) (2022)
- [Preference-grounded Token-level Guidance for Language Model Fine-tuning](https://mlsanity.com/paper/2306.00398/) (2023)
- [Prompting open-source and commercial language models for grammatical error correction of English learner text](https://mlsanity.com/paper/2401.07702/) (2024)
- [Teaching Llama a New Language Through Cross-Lingual Knowledge Transfer](https://mlsanity.com/paper/2404.04042/) (2024)
- [Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation](https://mlsanity.com/paper/2305.14734/) (2023)
- [Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets](https://mlsanity.com/paper/2302.13959/) (2023)
- [GPT-3.5 for Grammatical Error Correction](https://mlsanity.com/paper/2405.08469/) (2024)
- [Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language Models](https://mlsanity.com/paper/2305.14718/) (2023)
- [A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model](https://mlsanity.com/paper/2201.10707/) (2022)
- [Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation](https://mlsanity.com/paper/2205.10884/) (2022)
- [Interpretability for Language Learners Using Example-Based Grammatical Error Correction](https://mlsanity.com/paper/2203.07085/) (2022)
- [Beyond English: Evaluating LLMs for Arabic Grammatical Error Correction](https://mlsanity.com/paper/2312.08400/) (2023)
- [Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction](https://mlsanity.com/paper/2105.04443/) (2021)
- [Comparison of Grammatical Error Correction Using Back-Translation Models](https://mlsanity.com/paper/2104.07848/) (2021)
- [Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study](https://mlsanity.com/paper/2503.00985/) (2025)
- [System Combination for Grammatical Error Correction Based on Integer Programming](https://mlsanity.com/paper/2111.01465/) (2021)
- [From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2211.01625/) (2022)
- [Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction](https://mlsanity.com/paper/2303.14342/) (2023)
- [Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora](https://mlsanity.com/paper/2305.17906/) (2023)
- [SERRANT: a syntactic classifier for English Grammatical Error Types](https://mlsanity.com/paper/2104.02310/) (2021)
- [Do Grammatical Error Correction Models Realize Grammatical Generalization?](https://mlsanity.com/paper/2106.03031/) (2021)
- [An Extended Sequence Tagging Vocabulary for Grammatical Error Correction](https://mlsanity.com/paper/2302.05913/) (2023)
- [RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation](https://mlsanity.com/paper/2310.07299/) (2023)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [ChatGPT for Arabic Grammatical Error Correction](https://mlsanity.com/paper/2308.04492/) (2023)

## Abstract (arXiv)

Previously, neural methods in grammatical error correction (GEC) did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) baselines. We demonstrate parallels between neural GEC and low-resource neural MT and successfully adapt several methods from low-resource MT to neural GEC. We further establish guidelines for trustable results in neural GEC and propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. Proposed methods include adding source-side noise, domain-adaptation techniques, a GEC-specific training-objective, transfer learning with monolingual data, and ensembling of independently trained GEC models and language models. The combined effects of these methods result in better than state-of-the-art neural GEC models that outperform previously best neural GEC systems by more than 10% M2 on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set. Non-neural state-of-the-art systems are outperformed by more than 2% on the CoNLL-2014 benchmark and by 4% on JFLEG.