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It is organized by the Computational SLA 1 working group to support less represented languages in grammatical error detection and correction and related research areas. MultiGED datasets are constructed from second-language learner corpora, and the paper details the overall task, dataset generation process, and design decisions, along with evaluation metrics, Codalab setup, participant systems, and results.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/multiged-2023-shared-task-at-nlp4call-multilingual-grammatical-error-detection-paper/265435/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/multiged-2023-shared-task-at-nlp4call-multilingual-grammatical-error-detection-paper/265435.png","ImageObject",300,407,{"name":92,"@type":93},"Ben Jamin","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-14",true,{"@type":102,"interactionType":103,"userInteractionCount":46},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the MultiGED-2023 shared task about?","Question",{"text":112,"@type":113},"It focuses on Multilingual Grammatical Error Detection across five languages: Czech, English, German, Italian, and Swedish.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why was MultiGED-2023 organized, and by whom?",{"text":117,"@type":113},"It is the first shared task organized by the Computational SLA 1 working group, aiming to promote less represented languages in grammatical error detection/correction and related fields.",{"name":119,"@type":110,"acceptedAnswer":120},"How were the MultiGED datasets created?",{"text":121,"@type":113},"They were produced based on second language (L2) learner corpora for each specific language.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},265435,1789395748,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":46,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":36,"preview_page_count":4},2336478466772,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","MultiGED-2023 shared task at NLP4CALL: Multilingual Grammatical Error Detection  \nElena Volodina 1 , Christopher Bryant2 ,  \nAndrew Caines2 , Orphée De Clercq3 ,  \nJennifer-Carmen Frey4 , Elizaveta Ershova5 , Alexandr Rosen6 , Olga Vinogradova7  \n1 University of Gothenburg, Sweden, [elena.volodina@svenska.gu.se](elena.volodina@svenska.gu.se)[ ](elena.volodina@svenska.gu.se)2 ALTA Institute, University of Cambridge, UK, {cjb255,[apc38}@cam.ac.uk](apc38}@cam.ac.uk)  \n3 LT3, Ghent University, Belgium, [orphee.declercq@ugent.be](orphee.declercq@ugent.be)  \n4 EURAC Research, Italy, [JenniferCarmen.Frey@eurac.edu](JenniferCarmen.Frey@eurac.edu)  \n5 JetBrains, Cyprus, [elizaveta.ershova@jetbrains.com](elizaveta.ershova@jetbrains.com)  \n6 Charles University, Czech Republic, [alexandr.rosen@ff.cuni.cz](alexandr.rosen@ff.cuni.cz)  \n7 Independent researcher, Israel, [olgavinogr@gmail.com](olgavinogr@gmail.com)  \nAbstract  \nThis paper reports on the NLP4CALL shared task on Multilingual Grammatical Error Detection (MultiGED-2023), which included five languages: Czech, English, German, Italian and Swedish. It is the first shared task organized by the Computational SLA 1 working group, whose aim is to promote less represented languages in the fields of Grammatical Error Detection and Correction, and other related fields. The MultiGED datasets have been produced based on second language (L2) learner corpora for each particular language.  \nIn this paper we introduce the task as a whole, elaborate on the dataset generation process and the design choices made to obtain MultiGED datasets, provide details of the evaluation metrics and CodaLab setup. We further briefly describe the systems used by participants and report the results.  \n1 Introduction  \nShared tasks are competitions that challenge researchers around the world to solve practical research problems in controlled conditions (e.g., Nissim et al., 2017 ; Parra Escartín et al., 2017) . Within the field of (second) language acquisition  \nThis work is licensed under a Creative Commons Attribution 4 .0 International Licence. Licence details: [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/) .  \n1The acronym SLA stands for Second Language Acquisition. More information on the working group can be found here: [https://spraakbanken.gu.se/en/compsla](https://spraakbanken.gu.se/en/compsla)  \nand linguistic issues related to language learning, there have now been several shared tasks on various topics, including:  \n• argumentative essay analysis for feedback generation2 (e.g., Picou et al., 2021), where the challenge was to classify text sections into argumentative discourse elements, such as claim, rebuttal, evidence, etc.;  \n• essay grading / proficiency level prediction (e.g., Ballier et al., 2020), where, given an essay, the major task was to assign a corresponding CEFR proficiency level (A1, A2, B1, B2, etc);  \n• second language acquisition modeling (e.g., Settles et al., 2018), where the challenge was to predict where a learner might make an error given their error history;  \nMost prominent, though, have been challengeson so-called grammatical error detection (GED) and correction (GEC), where the task has been to either detect tokens in need of correction, or to produce a correction. Note that the attribute grammatical is used traditionally rather than descriptively, since other types of errors (e.g. lexical, orthographical, syntactical) are also targeted. GEC and GED have complemented each other over the years, and the historical interest in the two tasks is visualized in Figure 1 . In their comprehensive overview of approaches to GEC, Bryant et al.  \n2[https://www.kaggle.com/competitions/feedback-prize-](https://www.kaggle.com/competitions/feedback-prize-)[ ](https://www.kaggle.com/competitions/feedback-prize-)[2021/](2021/)  \nElena Volodina, Christopher Bryant, Andrew Caines, Orph´ee De Clercq, Jennifer-Carmen Frey, Elizaveta Ershova, Alexandr Rosen ","cbCaiqhvrMwBrRrs","https://ap.wps.com/l/cbCaiqhvrMwBrRrs","pdf",520304,16,"English","# Introduction\n## Shared tasks and related GED/GEC work\n## MultiGED-2023 dataset and evaluation overview\n## Systems and results","[{\"question\":\"What is the MultiGED-2023 shared task about?\",\"answer\":\"It focuses on Multilingual Grammatical Error Detection across five languages: Czech, English, German, Italian, and Swedish.\"},{\"question\":\"Why was MultiGED-2023 organized, and by whom?\",\"answer\":\"It is the first shared task organized by the Computational SLA 1 working group, aiming to promote less represented languages in grammatical error detection/correction and related fields.\"},{\"question\":\"How were the MultiGED datasets created?\",\"answer\":\"They were produced based on second language (L2) learner corpora for each specific language.\"}]","MultiGED-2023 shared task at NLP4CALL - Multilingual Grammatical Error Detection - paper | PDF"]