Context Based Approach for Second Language Acquisition

Nihal V. Nayak, Arjun R. Rao


Abstract
SLAM 2018 focuses on predicting a student’s mistake while using the Duolingo application. In this paper, we describe the system we developed for this shared task. Our system uses a logistic regression model to predict the likelihood of a student making a mistake while answering an exercise on Duolingo in all three language tracks - English/Spanish (en/es), Spanish/English (es/en) and French/English (fr/en). We conduct an ablation study with several features during the development of this system and discover that context based features plays a major role in language acquisition modeling. Our model beats Duolingo’s baseline scores in all three language tracks (AUROC scores for en/es = 0.821, es/en = 0.790 and fr/en = 0.812). Our work makes a case for providing favourable textual context for students while learning second language.
Anthology ID:
W18-0524
Volume:
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
BEA | NAACL | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
212–216
Language:
URL:
https://www.aclweb.org/anthology/W18-0524
DOI:
10.18653/v1/W18-0524
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-0524.pdf