TY  - RPRT
CY  - University of Engineering and Technology
ID  - SisLab2912
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2912/
A1  - Nguyen, Hong-Thinh
Y1  - 2017/12/15/
N2  - Although end-to-end deep neural network have gained popularity in the last few years and have been successful in several Natural Language Processing tasks, reading comprehension remains a challenging one. In this report, we presents in details the popular Bi-Directional Attention Flow model which represents the context at different level and combined the context-to-query and query-to-context direction attention. All necessary background knowledge of general Recurrent Neural Network is also discussed.
PB  - University of Engineering and Technology
KW  - RNN
KW  -  Natural Language Processing
M1  - technical_report
TI  - RNN on Machine Reading Comprehension Bi-Directional Attention Flow model
AV  - public
EP  - 17
ER  -