Multiple Admissibility in Language Learning: Judging Grammaticality Using Unlabeled Data

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
Number of pages11
Place of PublicationFlorence
PublisherThe Association for Computational Linguistics
Publication dateAug 2019
Pages12-22
ISBN (Print) 978-1-950737-41-3
Publication statusPublished - Aug 2019
MoE publication typeA4 Article in conference proceedings
EventThe 7th Workshop on
Balto-Slavic Natural Language Processing
: BSNLP
- , Italy
Duration: 2 Aug 20192 Aug 2019
http://bsnlp.cs.helsinki.fi

Fields of Science

  • 113 Computer and information sciences

Cite this

Katinskaia, A., Ivanova, S., & Yangarber, R. (2019). Multiple Admissibility in Language Learning: Judging Grammaticality Using Unlabeled Data. In The 7th Workshop on Balto-Slavic Natural Language Processing: Proceedings of the Workshop (pp. 12-22). The Association for Computational Linguistics. https://www.aclweb.org/anthology/W19-37#page=24