Multiple Admissibility in Language Learning: : Judging Grammaticality using Unlabeled Data

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dc.contributor.author Katinskaia, Anisia
dc.contributor.author Ivanova, Sardana
dc.contributor.author Yangarber, Roman
dc.contributor.editor Erjavec, Tomaž
dc.contributor.editor Marcińczuk, Michał
dc.contributor.editor Nakov, Preslav
dc.contributor.editor Piskorski, Jakub
dc.contributor.editor Pivovarova, Lidia
dc.contributor.editor Šnajder, Jan
dc.contributor.editor Steinberger, Josef
dc.contributor.editor Yangarber, Roman
dc.date.accessioned 2020-07-03T23:17:19Z
dc.date.available 2020-07-03T23:17:19Z
dc.date.issued 2019-08
dc.identifier.citation Katinskaia , A , Ivanova , S & Yangarber , R 2019 , Multiple Admissibility in Language Learning: Judging Grammaticality using Unlabeled Data . in T Erjavec , M Marcińczuk , P Nakov , J Piskorski , L Pivovarova , J Šnajder , J Steinberger & R Yangarber (eds) , The 7th Workshop on Balto-Slavic Natural Language Processing : Proceedings of the Workshop . The Association for Computational Linguistics , Stroudsburg , pp. 12-22 , Workshop on Balto-Slavic Natural Language Processing , Florence , Italy , 02/08/2019 . https://doi.org/10.18653/v1/W19-3702
dc.identifier.citation conference
dc.identifier.other PURE: 126210798
dc.identifier.other PURE UUID: e390c6a2-5b5a-4a69-a5aa-678c2f9a0337
dc.identifier.other ORCID: /0000-0001-5264-9870/work/68618678
dc.identifier.other WOS: 000538519300002
dc.identifier.uri http://hdl.handle.net/10138/317348
dc.description.abstract We present our work on the problem of detection Multiple Admissibility (MA) in language learning. Multiple Admissibility occurs when more than one grammatical form of a word fits syntactically and semantically in a given context. In second-language education—in particular, in intelligent tutoring systems/computer-aided language learning (ITS/CALL), systems generate exercises automatically. MA implies that multiple alternative answers are possible. We treat the problem as a grammaticality judgement task. We train a neural network with an objective to label sentences as grammatical or ungrammatical, using a "simulated learner corpus": a dataset with correct text and with artificial errors, generated automatically. While MA occurs commonly in many languages, this paper focuses on learning Russian. We present a detailed classification of the types of constructions in Russian, in which MA is possible, and evaluate the model using a test set built from answers provided by users of the Revita language learning system. en
dc.format.extent 11
dc.language.iso eng
dc.publisher The Association for Computational Linguistics
dc.relation.ispartof The 7th Workshop on Balto-Slavic Natural Language Processing
dc.relation.isversionof 978-1-950737-41-3
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 113 Computer and information sciences
dc.title Multiple Admissibility in Language Learning: : Judging Grammaticality using Unlabeled Data en
dc.type Conference contribution
dc.contributor.organization Department of Languages
dc.contributor.organization Department of Computer Science
dc.contributor.organization Department of Digital Humanities
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.18653/v1/W19-3702
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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