Towards better language representation in Natural Language Processing A multilingual dataset for text-level Grammatical Error Correction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11210%2F25%3A10513028" target="_blank" >RIV/00216208:11210/25:10513028 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/26:SHY6B8WN

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=uVkuQkp2Ul" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=uVkuQkp2Ul</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1075/ijlcr.24033.mas" target="_blank" >10.1075/ijlcr.24033.mas</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards better language representation in Natural Language Processing A multilingual dataset for text-level Grammatical Error Correction

Popis výsledku v původním jazyce

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 G E C 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 G E C 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 G E C studies.

Název v anglickém jazyce

Towards better language representation in Natural Language Processing A multilingual dataset for text-level Grammatical Error Correction

Popis výsledku anglicky

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 G E C 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 G E C 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 G E C studies.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

60203 - Linguistics

Projekt

<a href="/cs/project/LM2023044" target="_blank" >LM2023044: Český národní korpus</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Learner Corpus Research

ISSN

2215-1478

e-ISSN

2215-1486

Svazek periodika

11

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

27

Strana od-do

309-335

Kód UT WoS článku

001457603500001

EID výsledku v databázi Scopus

2-s2.0-105003035015