# Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses

arXiv: https://arxiv.org/abs/2010.07574 · HTML: https://mlsanity.com/paper/2010.07574/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 16 of 16)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [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)
- [MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2204.10994/) (2022)
- [Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond](https://mlsanity.com/paper/2205.11484/) (2022)
- [Czech Grammar Error Correction with a Large and Diverse Corpus](https://mlsanity.com/paper/2201.05590/) (2022)
- [FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2210.12364/) (2022)
- [NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts](https://mlsanity.com/paper/2305.16023/) (2023)
- [RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation](https://mlsanity.com/paper/2310.07299/) (2023)
- [Efficient and Interpretable Grammatical Error Correction with Mixture of Experts](https://mlsanity.com/paper/2410.23507/) (2024)
- [Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling](https://mlsanity.com/paper/2506.00064/) (2025)
- [RedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to Spans](https://mlsanity.com/paper/2309.10898/) (2023)
- [Organic Data-Driven Approach for Turkish Grammatical Error Correction and LLMs](https://mlsanity.com/paper/2405.15320/) (2024)
- [Tgea: An error-annotated dataset and benchmark tasks for text generation from pretrained language models](https://mlsanity.com/paper/2503.04232/) (2025)
- [Edit-level Majority Voting Mitigates Over-Correction in LLM-based Grammatical Error Correction](https://mlsanity.com/paper/2605.13624/) (2026)
- [Grammatical Error Correction Evaluation by Optimally Transporting Edit Representation](https://mlsanity.com/paper/2602.05419/) (2026)

## Abstract (arXiv)

Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum of GEC applications. We aim to broaden the target domain of GEC and release CWEB, a new benchmark for GEC consisting of website text generated by English speakers of varying levels of proficiency. Website data is a common and important domain that contains far fewer grammatical errors than learner essays, which we show presents a challenge to state-of-the-art GEC systems. We demonstrate that a factor behind this is the inability of systems to rely on a strong internal language model in low error density domains. We hope this work shall facilitate the development of open-domain GEC models that generalize to different topics and genres.