mlsanityData to 7 Oct 2026

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

arXiv 2103.16997 ↗First posted 31 Mar 2021cs.CLcited by 19

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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

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

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7 of 25 references.

References · 7

  1. 2020XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalizationcited by 738
  2. 2020GECToR -- Grammatical Error Correction: Tag, Not Rewritecited by 177
  3. 2017JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correctioncited by 96
  4. 2020Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Modelcited by 33
  5. 2019Grammatical Error Correction in Low-Resource Scenarioscited by 28
  6. 2020From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)?cited by 26
  7. 2018Can LSTM Learn to Capture Agreement? The Case of Basquecited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 3; 2024: 8; 2025: 0; 2026: 4.

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  1. 2024Large Language Models for Education: A Survey and Outlookcited by 185
  2. 2024Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuningcited by 174
  3. 2022Grammatical Error Correction: A Survey of the State of the Artcited by 43
  4. 2022MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correctioncited by 33
  5. 2023On the application of Large Language Models for language teaching and assessment technologycited by 26
  6. 2022Text Generation with Text-Editing Modelscited by 21
  7. 2024GPT-3.5 for Grammatical Error Correctioncited by 18
  8. 2022Czech Grammar Error Correction with a Large and Diverse Corpuscited by 17
  9. 2024Spivavtor: An Instruction Tuned Ukrainian Text Editing Modelcited by 4
  10. 2026Tokenizer Fertility and Zero-Shot Performance of Foundation Models on Ukrainian Legal Text: A Comparative Studycited by 4
  11. 2024Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trendscited by 3
  12. 2024To Err Is Human, but Llamas Can Learn It Toocited by 3
7 more
  1. 2024Setting up the Data Printer with Improved English to Ukrainian Machine Translationcited by 2
  2. 2026Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documentscited by 2
  3. 2023The Grammar and Syntax Based Corpus Analysis Tool For The Ukrainian Languagecited by 1
  4. 2023RedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to Spanscited by 1
  5. 2024Leveraging LLMs for Bangla Grammar Error Correction:Error Categorization, Synthetic Data, and Model Evaluationcited by 0
  6. 2026RoLegalGEC: Legal Domain Grammatical Error Detection and Correction Dataset for Romaniancited by 0
  7. 2026Larger Context Window, Fewer Overcorrections: Optimizing Prompts and Batching for Minimal-Edit Grammatical Error Correctioncited by 0