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GameQA: Gamified Mobile App Platform for Building Multiple-Domain Question-Answering Datasets

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dc.contributor.author Skarphedinsson, Njall
dc.contributor.author Gudmundsson, Breki
dc.contributor.author Smari, Steinar
dc.contributor.author Larusdottir, Marta Kristin
dc.contributor.author Einarsson, Hafsteinn
dc.contributor.author Khan, Abuzar
dc.contributor.author Nyberg, Eric
dc.contributor.author Loftsson, Hrafn
dc.date.accessioned 2023-06-23T01:29:27Z
dc.date.available 2023-06-23T01:29:27Z
dc.date.issued 2023-05-01
dc.identifier.citation Skarphedinsson , N , Gudmundsson , B , Smari , S , Larusdottir , M K , Einarsson , H , Khan , A , Nyberg , E & Loftsson , H 2023 , GameQA: Gamified Mobile App Platform for Building Multiple-Domain Question-Answering Datasets . in EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations : System Demonstrations . EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations , Association for Computational Linguistics , Dubrovnik, Croatia , pp. 152-160 . https://doi.org/10.18653/v1/2023.eacl-demo.18
dc.identifier.isbn 9781959429456
dc.identifier.other 132223240
dc.identifier.other 3c8de9e3-5c85-4895-b175-bb3eb493c1e2
dc.identifier.other Bibtex: skarphedinsson-etal-2023-gameqa
dc.identifier.other Bibtex: skarphedinsson-etal-2023-gameqa
dc.identifier.other 85159854462
dc.identifier.uri https://hdl.handle.net/20.500.11815/4314
dc.description Publisher Copyright: © 2023 Association for Computational Linguistics.
dc.description.abstract The methods used to create many of the well-known Question-Answering (QA) datasets are hard to replicate for low-resource languages. A commonality amongst these methods is hiring annotators to source answers from the internet by querying a single answer source, such as Wikipedia. Applying these methods for low-resource languages can be problematic since there is no single large answer source for these languages. Consequently, this can result in a high ratio of unanswered questions, since the amount of information in any single source is limited. To address this problem, we developed a novel crowd-sourcing platform to gather multiple-domain QA data for low-resource languages. Our platform, which consists of a mobile app and a web API, gamifies the data collection process. We successfully released the app for Icelandic (a low-resource language with about 350,000 native speakers) to build a dataset which rivals large QA datasets for high-resource languages both in terms of size and ratio of answered questions. We have made the platform open source with instructions on how to localize and deploy it to gather data for other low-resource languages.
dc.format.extent 9
dc.format.extent 1916206
dc.format.extent 152-160
dc.format.extent
dc.language.iso en
dc.publisher Association for Computational Linguistics
dc.relation.ispartofseries EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations; ()
dc.relation.ispartofseries EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations; ()
dc.rights info:eu-repo/semantics/openAccess
dc.title GameQA: Gamified Mobile App Platform for Building Multiple-Domain Question-Answering Datasets
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontobookanthology/conference
dc.description.version Peer reviewed
dc.identifier.doi 10.18653/v1/2023.eacl-demo.18
dc.relation.url http://www.scopus.com/inward/record.url?scp=85159854462&partnerID=8YFLogxK
dc.relation.url https://aclanthology.org/2023.eacl-demo.18/
dc.relation.url https://aclanthology.org/2023.eacl-demo.18
dc.contributor.department Department of Computer Science
dc.contributor.department Faculty of Industrial Engineering, Mechanical Engineering and Computer Science


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