Grotoco@SLAM: Second Language Acquisition Modeling with Simple Features, Learners and Task-wise Models

Sigrid Klerke, Héctor Martínez Alonso, Barbara Plank


Abstract
We present our submission to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We focus on evaluating a range of features for the task, including user-derived measures, while examining how far we can get with a simple linear classifier. Our analysis reveals that errors differ per exercise format, which motivates our final and best-performing system: a task-wise (per exercise-format) model.
Anthology ID:
W18-0523
Volume:
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Joel Tetreault, Jill Burstein, Ekaterina Kochmar, Claudia Leacock, Helen Yannakoudakis
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
206–211
Language:
URL:
https://aclanthology.org/W18-0523
DOI:
10.18653/v1/W18-0523
Bibkey:
Cite (ACL):
Sigrid Klerke, Héctor Martínez Alonso, and Barbara Plank. 2018. Grotoco@SLAM: Second Language Acquisition Modeling with Simple Features, Learners and Task-wise Models. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 206–211, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Grotoco@SLAM: Second Language Acquisition Modeling with Simple Features, Learners and Task-wise Models (Klerke et al., BEA 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-0523.pdf
Data
Universal Dependencies