CLUF: a Neural Model for Second Language Acquisition Modeling

Shuyao Xu, Jin Chen, Long Qin


Abstract
Second Language Acquisition Modeling is the task to predict whether a second language learner would respond correctly in future exercises based on their learning history. In this paper, we propose a neural network based system to utilize rich contextual, linguistic and user information. Our neural model consists of a Context encoder, a Linguistic feature encoder, a User information encoder and a Format information encoder (CLUF). Furthermore, a decoder is introduced to combine such encoded features and make final predictions. Our system ranked in first place in the English track and second place in the Spanish and French track with an AUROC score of 0.861, 0.835 and 0.854 respectively.
Anthology ID:
W18-0546
Volume:
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
BEA | NAACL | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
374–380
Language:
URL:
https://aclanthology.org/W18-0546
DOI:
10.18653/v1/W18-0546
Bibkey:
Cite (ACL):
Shuyao Xu, Jin Chen, and Long Qin. 2018. CLUF: a Neural Model for Second Language Acquisition Modeling. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 374–380, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
CLUF: a Neural Model for Second Language Acquisition Modeling (Xu et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-0546.pdf