# Grammatical Error Correction: A Survey of the State of the Art

arXiv: https://arxiv.org/abs/2211.05166 · HTML: https://mlsanity.com/paper/2211.05166/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 87 of its 273 references that are in the map.
- First posted on arXiv 9 Nov 2022.

## Builds on (most cited first, 30 of 87)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [GECToR -- Grammatical Error Correction: Tag, Not Rewrite](https://mlsanity.com/paper/2005.12592/) (2020)
- [A Simple Recipe for Multilingual Grammatical Error Correction](https://mlsanity.com/paper/2106.03830/) (2021)
- [Encode, Tag, Realize: High-Precision Text Editing](https://mlsanity.com/paper/1909.01187/) (2019)
- [JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction](https://mlsanity.com/paper/1702.04066/) (2017)
- [Parallel Iterative Edit Models for Local Sequence Transduction](https://mlsanity.com/paper/1910.02893/) (2019)
- [Break-It-Fix-It: Unsupervised Learning for Program Repair](https://mlsanity.com/paper/2106.06600/) (2021)
- [Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction](https://mlsanity.com/paper/2005.00987/) (2020)
- [Frequency Effects on Syntactic Rule Learning in Transformers](https://mlsanity.com/paper/2109.07020/) (2021)
- [Seq2Edits: Sequence Transduction Using Span-level Edit Operations](https://mlsanity.com/paper/2009.11136/) (2020)
- [Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models](https://mlsanity.com/paper/2105.13318/) (2021)
- [An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction](https://mlsanity.com/paper/1909.00502/) (2019)

## Built on by (most cited first, 30 of 43)

- [ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark](https://mlsanity.com/paper/2303.13648/) (2023)
- [Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance](https://mlsanity.com/paper/2305.13225/) (2023)
- [On the application of Large Language Models for language teaching and assessment technology](https://mlsanity.com/paper/2307.08393/) (2023)
- [Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation](https://mlsanity.com/paper/2305.14734/) (2023)
- [Resilience of Large Language Models for Noisy Instructions](https://mlsanity.com/paper/2404.09754/) (2024)
- [How Ready Are Generative Pre-trained Large Language Models for Explaining Bengali Grammatical Errors?](https://mlsanity.com/paper/2406.00039/) (2024)
- [Robustness of Large Language Models to Perturbations in Text](https://mlsanity.com/paper/2407.08989/) (2024)
- [NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts](https://mlsanity.com/paper/2305.16023/) (2023)
- [Rubrik's Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset](https://mlsanity.com/paper/2503.23899/) (2025)
- [OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch](https://mlsanity.com/paper/2309.10706/) (2023)
- [Gender-Inclusive Grammatical Error Correction through Augmentation](https://mlsanity.com/paper/2306.07415/) (2023)
- [RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation](https://mlsanity.com/paper/2310.07299/) (2023)
- [Towards End-to-End Spoken Grammatical Error Correction](https://mlsanity.com/paper/2311.05550/) (2023)
- [CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction](https://mlsanity.com/paper/2305.10819/) (2023)
- [Evaluating GPT-3.5 and GPT-4 on Grammatical Error Correction for Brazilian Portuguese](https://mlsanity.com/paper/2306.15788/) (2023)
- [ChatGPT for Arabic Grammatical Error Correction](https://mlsanity.com/paper/2308.04492/) (2023)
- [CSynGEC: Incorporating Constituent-based Syntax for Grammatical Error Correction with a Tailored GEC-Oriented Parser](https://mlsanity.com/paper/2211.08158/) (2022)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [Revisiting Meta-evaluation for Grammatical Error Correction](https://mlsanity.com/paper/2403.02674/) (2024)
- [APRES: An Agentic Paper Revision and Evaluation System](https://mlsanity.com/paper/2603.03142/) (2026)
- [Oddballness: universal anomaly detection with language models](https://mlsanity.com/paper/2409.03046/) (2024)
- [Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications](https://mlsanity.com/paper/2505.18488/) (2025)
- [Scaling and Prompting for Improved End-to-End Spoken Grammatical Error Correction](https://mlsanity.com/paper/2505.21137/) (2025)
- [A BERT-based Unsupervised Grammatical Error Correction Framework](https://mlsanity.com/paper/2303.17367/) (2023)
- [Are Pre-trained Language Models Useful for Model Ensemble in Chinese Grammatical Error Correction?](https://mlsanity.com/paper/2305.15183/) (2023)
- [Grammatical Error Correction via Mixed-Grained Weighted Training](https://mlsanity.com/paper/2311.13848/) (2023)
- [Semantically Corrected Amharic Automatic Speech Recognition](https://mlsanity.com/paper/2404.13362/) (2024)
- [Tibyan Corpus: Balanced and Comprehensive Error Coverage Corpus Using ChatGPT for Arabic Grammatical Error Correction](https://mlsanity.com/paper/2411.04588/) (2024)
- [End-to-End Spoken Grammatical Error Correction](https://mlsanity.com/paper/2506.18532/) (2025)
- [Data Augmentation for Spoken Grammatical Error Correction](https://mlsanity.com/paper/2507.19374/) (2025)

## Abstract (arXiv)

Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject-verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems which represent the current dominant state of the art. In this survey paper, we condense the field into a single article and first outline some of the linguistic challenges of the task, introduce the most popular datasets that are available to researchers (for both English and other languages), and summarise the various methods and techniques that have been developed with a particular focus on artificial error generation. We next describe the many different approaches to evaluation as well as concerns surrounding metric reliability, especially in relation to subjective human judgements, before concluding with an overview of recent progress and suggestions for future work and remaining challenges. We hope that this survey will serve as comprehensive resource for researchers who are new to the field or who want to be kept apprised of recent developments.