?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.relation=https%3A%2F%2Feprints.uet.vnu.edu.vn%2Feprints%2Fid%2Feprint%2F2912%2F&rft.title=RNN+on+Machine+Reading+Comprehension+Bi-Directional+Attention+Flow+model&rft.creator=Nguyen%2C+Hong-Thinh&rft.subject=Electronics+and+Communications&rft.description=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%2C+reading+comprehension+remains+a+challenging+one.+In+this+report%2C+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.&rft.publisher=University+of+Engineering+and+Technology&rft.date=2017-12-15&rft.type=Technical+Report&rft.type=NonPeerReviewed&rft.format=application%2Fpdf&rft.language=en&rft.identifier=https%3A%2F%2Feprints.uet.vnu.edu.vn%2Feprints%2Fid%2Feprint%2F2912%2F1%2Ftechnical%2520report.pdf&rft.identifier=++Nguyen%2C+Hong-Thinh++(2017)+RNN+on+Machine+Reading+Comprehension+Bi-Directional+Attention+Flow+model.++Technical+Report.+University+of+Engineering+and+Technology%2C+University+of+Engineering+and+Technology.+++++