@inproceedings{katinskaia-ivanova-2019-multiple,
title = "Multiple Admissibility: Judging Grammaticality using Unlabeled Data in Language Learning",
author = "Katinskaia, Anisia and
Ivanova, Sardana",
editor = "Erjavec, Toma{\v{z}} and
Marci{\'n}czuk, Micha{\l} and
Nakov, Preslav and
Piskorski, Jakub and
Pivovarova, Lidia and
{\v{S}}najder, Jan and
Steinberger, Josef and
Yangarber, Roman",
booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3702",
doi = "10.18653/v1/W19-3702",
pages = "12--22",
abstract = "We present our work on the problem of Multiple Admissibility (MA) in language learning. Multiple Admissibility occurs in many languages when more than one grammatical form of a word fits syntactically and semantically in a given context. In second language (L2) education - in particular, in intelligent tutoring systems/computer-aided language learning (ITS/CALL) systems, which generate exercises automatically - this 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 the users of a running language learning system.",
}
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%0 Conference Proceedings
%T Multiple Admissibility: Judging Grammaticality using Unlabeled Data in Language Learning
%A Katinskaia, Anisia
%A Ivanova, Sardana
%Y Erjavec, Tomaž
%Y Marcińczuk, Michał
%Y Nakov, Preslav
%Y Piskorski, Jakub
%Y Pivovarova, Lidia
%Y Šnajder, Jan
%Y Steinberger, Josef
%Y Yangarber, Roman
%S Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F katinskaia-ivanova-2019-multiple
%X We present our work on the problem of Multiple Admissibility (MA) in language learning. Multiple Admissibility occurs in many languages when more than one grammatical form of a word fits syntactically and semantically in a given context. In second language (L2) education - in particular, in intelligent tutoring systems/computer-aided language learning (ITS/CALL) systems, which generate exercises automatically - this 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 the users of a running language learning system.
%R 10.18653/v1/W19-3702
%U https://aclanthology.org/W19-3702
%U https://doi.org/10.18653/v1/W19-3702
%P 12-22
Markdown (Informal)
[Multiple Admissibility: Judging Grammaticality using Unlabeled Data in Language Learning](https://aclanthology.org/W19-3702) (Katinskaia & Ivanova, BSNLP 2019)
ACL