Towards better language representation in natural language processing : a multilingual dataset for text-level grammatical error correction

Masciolini, Arianna (author), Caines, Andrew (author), Arhar Holdt, Špela (author), Žagar, Aleš (author)

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.

• Language: English
• Keywords: learner corpora, grammatical error correction, multilingual corpora, Matthew effect, MultiGEC shared task
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: Str. 309-335
• Numbering: Vol. 11, iss. 2
• PID: 20.500.12556/RUL-172814
• UDC: 81'322.2:81'36
• ISSN on article: 2215-1478
• DOI: 10.1075/ijlcr.24033.mas
• COBISS.SI-ID: 234594051
• Publication date in RUL: 11.09.2025

Record is a part of a journal

• Title: International journal of learner corpus research
• Publisher: J. Benjamins
• ISSN: 2215-1478
• COBISS.SI-ID: 522804761

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: učni korpusi, popravljanje slovničnih napak, večjezični korpusi, Matejev učinek, skupna naloga MultiGEC