Abstract
We introduce the TMU systems for the second language acquisition modeling shared task 2018 (Settles et al., 2018). To model learner error patterns, it is necessary to maintain a considerable amount of information regarding the type of exercises learners have been learning in the past and the manner in which they answered them. Tracking an enormous learner’s learning history and their correct and mistaken answers is essential to predict the learner’s future mistakes. Therefore, we propose a model which tracks the learner’s learning history efficiently. Our systems ranked fourth in the English and Spanish subtasks, and fifth in the French subtask.- Anthology ID:
- W18-0544
- 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:
- 365–369
- Language:
- URL:
- https://aclanthology.org/W18-0544
- DOI:
- 10.18653/v1/W18-0544
- Cite (ACL):
- Masahiro Kaneko, Tomoyuki Kajiwara, and Mamoru Komachi. 2018. TMU System for SLAM-2018. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 365–369, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- TMU System for SLAM-2018 (Kaneko et al., BEA 2018)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/W18-0544.pdf
- Code
- kanekomasahiro/SLAM18_model