# UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language

arXiv: https://arxiv.org/abs/2103.16997 · HTML: https://mlsanity.com/paper/2103.16997/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 31 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [GECToR -- Grammatical Error Correction: Tag, Not Rewrite](https://mlsanity.com/paper/2005.12592/) (2020)
- [JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction](https://mlsanity.com/paper/1702.04066/) (2017)
- [Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model](https://mlsanity.com/paper/2005.11849/) (2020)
- [Grammatical Error Correction in Low-Resource Scenarios](https://mlsanity.com/paper/1910.00353/) (2019)
- [From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)?](https://mlsanity.com/paper/2005.01330/) (2020)
- [Can LSTM Learn to Capture Agreement? The Case of Basque](https://mlsanity.com/paper/1809.04022/) (2018)

## Built on by (most cited first, 19 of 19)

- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning](https://mlsanity.com/paper/2402.06619/) (2024)
- [Grammatical Error Correction: A Survey of the State of the Art](https://mlsanity.com/paper/2211.05166/) (2022)
- [MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2204.10994/) (2022)
- [On the application of Large Language Models for language teaching and assessment technology](https://mlsanity.com/paper/2307.08393/) (2023)
- [Text Generation with Text-Editing Models](https://mlsanity.com/paper/2206.07043/) (2022)
- [GPT-3.5 for Grammatical Error Correction](https://mlsanity.com/paper/2405.08469/) (2024)
- [Czech Grammar Error Correction with a Large and Diverse Corpus](https://mlsanity.com/paper/2201.05590/) (2022)
- [Spivavtor: An Instruction Tuned Ukrainian Text Editing Model](https://mlsanity.com/paper/2404.18880/) (2024)
- [Tokenizer Fertility and Zero-Shot Performance of Foundation Models on Ukrainian Legal Text: A Comparative Study](https://mlsanity.com/paper/2605.14890/) (2026)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [To Err Is Human, but Llamas Can Learn It Too](https://mlsanity.com/paper/2403.05493/) (2024)
- [Setting up the Data Printer with Improved English to Ukrainian Machine Translation](https://mlsanity.com/paper/2404.15196/) (2024)
- [Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents](https://mlsanity.com/paper/2608.12342/) (2026)
- [The Grammar and Syntax Based Corpus Analysis Tool For The Ukrainian Language](https://mlsanity.com/paper/2305.13530/) (2023)
- [RedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to Spans](https://mlsanity.com/paper/2309.10898/) (2023)
- [Leveraging LLMs for Bangla Grammar Error Correction:Error Categorization, Synthetic Data, and Model Evaluation](https://mlsanity.com/paper/2406.14284/) (2024)
- [RoLegalGEC: Legal Domain Grammatical Error Detection and Correction Dataset for Romanian](https://mlsanity.com/paper/2604.19593/) (2026)
- [Larger Context Window, Fewer Overcorrections: Optimizing Prompts and Batching for Minimal-Edit Grammatical Error Correction](https://mlsanity.com/paper/2609.10810/) (2026)

## Abstract (arXiv)

We present a corpus professionally annotated for grammatical error correction (GEC) and fluency edits in the Ukrainian language. To the best of our knowledge, this is the first GEC corpus for the Ukrainian language. We collected texts with errors (20,715 sentences) from a diverse pool of contributors, including both native and non-native speakers. The data cover a wide variety of writing domains, from text chats and essays to formal writing. Professional proofreaders corrected and annotated the corpus for errors relating to fluency, grammar, punctuation, and spelling. This corpus can be used for developing and evaluating GEC systems in Ukrainian. More generally, it can be used for researching multilingual and low-resource NLP, morphologically rich languages, document-level GEC, and fluency correction. The corpus is publicly available at https://github.com/grammarly/ua-gec