@inproceedings{vie-2018-deep,
title = "Deep Factorization Machines for Knowledge Tracing",
author = "Vie, Jill-J{\^e}nn",
editor = "Tetreault, Joel and
Burstein, Jill and
Kochmar, Ekaterina and
Leacock, Claudia and
Yannakoudakis, Helen",
booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-0545",
doi = "10.18653/v1/W18-0545",
pages = "370--373",
abstract = "This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning model of pairwise relationships between users, items, skills, and other entities considered. Our solution (AUC 0.815) hopefully managed to beat the logistic regression baseline (AUC 0.774) but not the top performing model (AUC 0.861) and reveals interesting strategies to build upon item response theory models.",
}
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%0 Conference Proceedings
%T Deep Factorization Machines for Knowledge Tracing
%A Vie, Jill-Jênn
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F vie-2018-deep
%X This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning model of pairwise relationships between users, items, skills, and other entities considered. Our solution (AUC 0.815) hopefully managed to beat the logistic regression baseline (AUC 0.774) but not the top performing model (AUC 0.861) and reveals interesting strategies to build upon item response theory models.
%R 10.18653/v1/W18-0545
%U https://aclanthology.org/W18-0545
%U https://doi.org/10.18653/v1/W18-0545
%P 370-373
Markdown (Informal)
[Deep Factorization Machines for Knowledge Tracing](https://aclanthology.org/W18-0545) (Vie, BEA 2018)
ACL
- Jill-Jênn Vie. 2018. Deep Factorization Machines for Knowledge Tracing. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 370–373, New Orleans, Louisiana. Association for Computational Linguistics.