# Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation

arXiv: https://arxiv.org/abs/2210.14389 · HTML: https://mlsanity.com/paper/2210.14389/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 56 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Corpora Generation for Grammatical Error Correction](https://mlsanity.com/paper/1904.05780/) (2019)
- [Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data](https://mlsanity.com/paper/1903.00138/) (2019)
- [Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model](https://mlsanity.com/paper/2005.11849/) (2020)
- [Automatic Error Type Annotation for Arabic](https://mlsanity.com/paper/2109.08068/) (2021)
- [Automatic Metric Validation for Grammatical Error Correction](https://mlsanity.com/paper/1804.11225/) (2018)
- [Czech Grammar Error Correction with a Large and Diverse Corpus](https://mlsanity.com/paper/2201.05590/) (2022)
- [From Characters to Words to in Between: Do We Capture Morphology?](https://mlsanity.com/paper/1704.08352/) (2017)

## Built on by (most cited first, 13 of 13)

- [mEdIT: Multilingual Text Editing via Instruction Tuning](https://mlsanity.com/paper/2402.16472/) (2024)
- [KatFishNet: Detecting LLM-Generated Korean Text through Linguistic Feature Analysis](https://mlsanity.com/paper/2503.00032/) (2025)
- [Toward Practical Automatic Speech Recognition and Post-Processing: a Call for Explainable Error Benchmark Guideline](https://mlsanity.com/paper/2401.14625/) (2024)
- [Polishing Every Facet of the GEM: Testing Linguistic Competence of LLMs and Humans in Korean](https://mlsanity.com/paper/2506.01237/) (2025)
- [Multilingual Grammatical Error Annotation: Combining Language-Agnostic Framework with Language-Specific Flexibility](https://mlsanity.com/paper/2506.07719/) (2025)
- [KoGEC : Korean Grammatical Error Correction with Pre-trained Translation Models](https://mlsanity.com/paper/2506.11432/) (2025)
- [Edit-level Majority Voting Mitigates Over-Correction in LLM-based Grammatical Error Correction](https://mlsanity.com/paper/2605.13624/) (2026)
- [Neural Automated Writing Evaluation with Corrective Feedback](https://mlsanity.com/paper/2402.17613/) (2024)
- [Making Sense of Korean Sentences: A Comprehensive Evaluation of LLMs through KoSEnd Dataset](https://mlsanity.com/paper/2507.03378/) (2025)
- [SCRIPT: A Subcharacter Compositional Representation Injection Module for Korean Pre-Trained Language Models](https://mlsanity.com/paper/2604.12377/) (2026)
- [Refining Word-Based Grammatical Error Annotation for L2 Korean](https://mlsanity.com/paper/2605.30545/) (2026)
- [Lexically conditioned realization ambiguity in Korean predicate morphology](https://mlsanity.com/paper/2608.27966/) (2026)
- [Quantifying and Mitigating Korean Jamo-Level Typographical Vulnerabilities in Large Language Models](https://mlsanity.com/paper/2608.30229/) (2026)

## Abstract (arXiv)

Research on Korean grammatical error correction (GEC) is limited, compared to other major languages such as English. We attribute this problematic circumstance to the lack of a carefully designed evaluation benchmark for Korean GEC. In this work, we collect three datasets from different sources (Kor-Lang8, Kor-Native, and Kor-Learner) that covers a wide range of Korean grammatical errors. Considering the nature of Korean grammar, We then define 14 error types for Korean and provide KAGAS (Korean Automatic Grammatical error Annotation System), which can automatically annotate error types from parallel corpora. We use KAGAS on our datasets to make an evaluation benchmark for Korean, and present baseline models trained from our datasets. We show that the model trained with our datasets significantly outperforms the currently used statistical Korean GEC system (Hanspell) on a wider range of error types, demonstrating the diversity and usefulness of the datasets. The implementations and datasets are open-sourced.