Multiple Admissibility: Judging Grammaticality using Unlabeled Data in Language Learning

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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.
Original languageEnglish
Title of host publicationThe 7th Workshop on Balto-Slavic Natural Language Processing : Proceedings of the Workshop
EditorsTomaž Erjavec, Michał Marcińczuk, Preslav Nakov, Jakub Piskorski, Lidia Pivovarova, Jan Šnajder, Josef Steinberger, Roman Yangarber
Number of pages11
Place of PublicationStroudsburg
PublisherThe Association for Computational Linguistics
Publication dateAug 2019
Pages12-22
ISBN (Electronic)978-1-950737-41-3
DOIs
Publication statusPublished - Aug 2019
MoE publication typeA4 Article in conference proceedings
EventWorkshop on
Balto-Slavic Natural Language Processing
- Florence, Italy
Duration: 2 Aug 20192 Aug 2019
Conference number: 7
http://bsnlp.cs.helsinki.fi

Fields of Science

  • 113 Computer and information sciences

Cite this

Katinskaia, A., Ivanova, S., & Yangarber, R. (2019). Multiple Admissibility: Judging Grammaticality using Unlabeled Data in Language Learning. 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 (pp. 12-22). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-3702