peterua/OmniGEC-ModelTraining

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The declared licence (mit) is classified as permissive.

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Citation text from the card

@inproceedings{kovalchuk-etal-2025-introducing,
    title = "Introducing {O}mni{GEC}: A Silver Multilingual Dataset for Grammatical Error Correction",
    author = "Kovalchuk, Roman  and
      Romanyshyn, Mariana  and
      Ivaniuk, Petro",
    editor = "Romanyshyn, Mariana",
    booktitle = "Proceedings of the Fourth Ukrainian Natural Language Processing Workshop (UNLP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.unlp-1.17/",
    pages = "162--178",
    ISBN = "979-8-89176-269-5",
    abstract = "In this paper, we introduce OmniGEC, a collection of multilingual silver-standard datasets for the task of Grammatical Error Correction (GEC), covering eleven languages: Czech, English, Estonian, German, Greek, Icelandic, Italian, Latvian, Slovene, Swedish, and Ukrainian. These datasets facilitate the development of multilingual GEC solutions and help bridge the data gap in adapting English GEC solutions to multilingual GEC. The texts in the datasets originate from three sources: Wikipedia edits for the eleven target languages, subreddits from Reddit in the eleven target languages, and the Ukrainian-only UberText 2.0 social media corpus. While Wikipedia edits were derived from human-made corrections, the Reddit and UberText 2.0 data were automatically corrected with the GPT-4o-mini model. The quality of the corrections in the datasets was eva

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dataset_licence({ "id": "peterua/OmniGEC-ModelTraining" })
signal: "permissive"
stated_licence: "mit"
sources: 0