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

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

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.

• Jezik: Angleški jezik
• Ključne besede: learner corpora, grammatical error correction, multilingual corpora, Matthew effect, MultiGEC shared task
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: Str. 309-335
• Številčenje: Vol. 11, iss. 2
• PID: 20.500.12556/RUL-172814
• UDK: 81'322.2:81'36
• ISSN pri članku: 2215-1478
• DOI: 10.1075/ijlcr.24033.mas
• COBISS.SI-ID: 234594051
• Datum objave v RUL: 11.09.2025

Gradivo je del revije

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

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: učni korpusi, popravljanje slovničnih napak, večjezični korpusi, Matejev učinek, skupna naloga MultiGEC