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://www.aclweb.org/anthology/W18-0506
DOI:
10.18653/v1/W18-0506
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-0506.pdf