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T1  - Multiple Admissibility in Language Learning: : Judging Grammaticality using Unlabeled Data 
SN  -  /  
UR  - http://hdl.handle.net/10138/317348 
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A1  - Katinskaia, Anisia; Ivanova, Sardana; Yangarber, Roman 
A2  - Erjavec, Tomaž; Marcińczuk, Michał; Nakov, Preslav; Piskorski, Jakub; Pivovarova, Lidia; Šnajder, Jan; Steinberger, Josef; Yangarber, Roman 
PB  - The Association for Computational Linguistics 
Y1  - 2019 
LA  - eng 
AB  - 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 judgeme... 
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KW  - 113 Computer and information sciences 
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