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.- Anthology ID:
- W19-3702
- Volume:
- Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- BSNLP
- SIG:
- SIGSLAV
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12–22
- Language:
- URL:
- https://aclanthology.org/W19-3702
- DOI:
- 10.18653/v1/W19-3702
- Cite (ACL):
- Anisia Katinskaia and Sardana Ivanova. 2019. Multiple Admissibility: Judging Grammaticality using Unlabeled Data in Language Learning. In Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing, pages 12–22, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Multiple Admissibility: Judging Grammaticality using Unlabeled Data in Language Learning (Katinskaia & Ivanova, BSNLP 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W19-3702.pdf