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.
Cite
CITATION STYLE
Kaneko, M., Kajiwara, T., & Komachi, M. (2018). TMU system for SLAM-2018. In Proceedings of the 13th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018 (pp. 365–369). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w18-0544
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