Multiple Admissibility in Language Learning: Judging Grammaticality Using Unlabeled Data

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

Abstrakti

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.
Alkuperäiskielienglanti
OtsikkoThe 7th Workshop on Balto-Slavic Natural Language Processing : Proceedings of the Workshop
Sivumäärä11
JulkaisupaikkaFlorence
KustantajaThe Association for Computational Linguistics
Julkaisupäiväelokuuta 2019
Sivut12-22
ISBN (painettu) 978-1-950737-41-3
TilaJulkaistu - elokuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaThe 7th Workshop on
Balto-Slavic Natural Language Processing
: BSNLP
- , Italia
Kesto: 2 elokuuta 20192 elokuuta 2019
http://bsnlp.cs.helsinki.fi

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Siteeraa tätä

Katinskaia, A., Ivanova, S., & Yangarber, R. (2019). Multiple Admissibility in Language Learning: Judging Grammaticality Using Unlabeled Data. teoksessa The 7th Workshop on Balto-Slavic Natural Language Processing: Proceedings of the Workshop (Sivut 12-22). Florence: The Association for Computational Linguistics.