Second Language Acquisition Modeling

Burr Settles, Chris Brust, Erin Gustafson, Masato Hagiwara, Nitin Madnani


Abstract
We present the task of second language acquisition (SLA) modeling. Given a history of errors made by learners of a second language, the task is to predict errors that they are likely to make at arbitrary points in the future. We describe a large corpus of more than 7M words produced by more than 6k learners of English, Spanish, and French using Duolingo, a popular online language-learning app. Then we report on the results of a shared task challenge aimed studying the SLA task via this corpus, which attracted 15 teams and synthesized work from various fields including cognitive science, linguistics, and machine learning.
Anthology ID:
W18-0506
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:
56–65
Language:
URL:
https://aclanthology.org/W18-0506
DOI:
10.18653/v1/W18-0506
Bibkey:
Cite (ACL):
Burr Settles, Chris Brust, Erin Gustafson, Masato Hagiwara, and Nitin Madnani. 2018. Second Language Acquisition Modeling. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 56–65, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Second Language Acquisition Modeling (Settles et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-0506.pdf
Data
Duolingo SLAM Shared Task