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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W18-0523.pdf
- Data
- Universal Dependencies