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://aclanthology.org/W18-0524
DOI:
10.18653/v1/W18-0524
Bibkey:
Cite (ACL):
Nihal V. Nayak and Arjun R. Rao. 2018. Context Based Approach for Second Language Acquisition. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 212–216, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Context Based Approach for Second Language Acquisition (Nayak & Rao, 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/W18-0524.pdf
Code
 additional community code
Data
Duolingo SLAM Shared Task