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Review article published In: International Journal of Learner Corpus Research
Vol. 11:2 (2025) ► pp.309–335

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Corpus report

Towards better language representation in Natural Language Processing

A multilingual dataset for text-level Grammatical Error Correction

ORCiD logo with link | Språkbanken Text, SFS University of Gothenburg
 | University of Cambridge
 | Ghent University
 | Ghent University
 | RISE Research Institutes of Sweden
 | Språkbanken Text, SFS University of Gothenburg
 | Språkbanken Text, SFS University of Gothenburg
 | Stockholm University
 | Tallinn University
 | University of Ljubljana
 | University of Latvia
 | University of Latvia
 | Aristotle University of Thessaloniki
 | Eurac Research Bolzano
 | University of Iceland
 | Aristotle University of Thessaloniki
 | Eurac Research Bolzano
 | Grammarly
 | Charles University
 | City University of New York
 | Institute of the Estonian Language
 | Microsoft
 | Aristotle University of Thessaloniki
 | Aristotle University of Thessaloniki
 | Aristotle University of Thessaloniki
 | Aristotle University of Thessaloniki
 | Aristotle University of Thessaloniki
 | Leipzig University
 | University of Ljubljana
 | FernUniversität in Hagen
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Available under the Creative Commons Attribution (CC BY) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Open Access publication of this article was funded through a Transformative Agreement with University of Gothenburg.

Published online: 1 April 2025
DOI logo with link
https://doi.org/10.1075/ijlcr.24033.mas
Abstract
This paper introduces MultiGEC, a dataset for multilingual Grammatical Error Correction (GEC) in twelve European languages: Czech, English, Estonian, German, Greek, Icelandic, Italian, Latvian, Russian, Slovene, Swedish and Ukrainian. MultiGEC distinguishes itself from previous GEC datasets in that it covers several underrepresented languages, which we argue should be included in resources used to train models for Natural Language Processing tasks which, as GEC itself, have implications for Learner Corpus Research and Second Language Acquisition. Aside from multilingualism, the novelty of the MultiGEC dataset is that it consists of full texts — typically learner essays — rather than individual sentences, making it possible to train systems that take a broader context into account. The dataset was built for MultiGEC-2025, the first shared task in multilingual text-level GEC, but it remains accessible after its competitive phase, serving as a resource to train new error correction systems and perform cross-lingual GEC studies.
Article outline
  • 1.Introduction
  • 2.Data
    • 2.1Czech
    • 2.2English
    • 2.3Estonian
    • 2.4German
    • 2.5Greek
    • 2.6Icelandic
    • 2.7Italian
    • 2.8Latvian
    • 2.9Russian
    • 2.10Slovene
    • 2.11Swedish
    • 2.12Ukrainian
  • 3.Conclusions and future outlook
  • Open data badge and data availability statement
  • Notes
  • References
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