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Automatic Question Tagging with Deep Neural Networks - 2018

Automatic Question Tagging With Deep Neural Networks

Research Paper on Automatic Question Tagging With Deep Neural Networks

Research Area:  Machine Learning


In recent years, computerized adaptive testing (CAT) has gained popularity as an important means to evaluate students ability. Assigning tags to test questions is crucial in CAT. Manual tagging is widely used for constructing question banks; however, this approach is time-consuming and might lead to consistency issues. Automatic question tagging, an alternative, has not been studied extensively. In this paper, we propose a position-based attention model and keywords-based model to automatically tag questions with knowledge units. With regard to multiple-choice questions, the proposed models employ mechanisms to capture useful information from keywords to enhance tagging performance. Unlike traditional machine learning-based tagging methods, our models utilize deep neural networks to represent questions using contextual information. The experimental results show that our proposed models outperform some traditional classification and topic methods by a large margin on an English question bank dataset.

Automatic Question Tagging
Deep Neural Networks
Machine Learning
Deep Learning
computerized adaptive testing (CAT)

Author(s) Name:  Bo Sun; Yunzong Zhu; Yongkang Xiao; Rong Xiao and Yungang Wei

Journal name:  IEEE Transactions on Learning Technologies

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/TLT.2018.2808187

Volume Information:  Volume: 12, Issue: 1, Jan.-March 1 2019, Page(s): 29 - 43