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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/8228
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dc.contributor.authorSharma, Yashvardhan-
dc.date.accessioned2023-01-02T11:04:33Z-
dc.date.available2023-01-02T11:04:33Z-
dc.date.issued2018-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1877050918308226-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8228-
dc.description.abstractQuestion Answering (QA) System is very useful as most of the deep learning related problems can be modeled as a question answering problem. Consequently, the field is one of the most researched fields in computer science today. The last few years have seen considerable developments and improvement in the state of the art, much of which can be credited to upcoming of Deep Learning. In this paper, a discussion about various approaches starting from the basic NLP and algorithms based approach has been done and the paper eventually builds towards the recently proposed methods of Deep Learning. Implementation details and various tweaks in the algorithms that produced better results have also been discussed. The evaluation of the proposed models was done on twenty tasks of babI dataset of Facebook.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectComputer Scienceen_US
dc.subjectCoattentionen_US
dc.subjectDeep Learningen_US
dc.subjectMemory netsen_US
dc.subjectNeural networksen_US
dc.subjectQuestion answeringen_US
dc.titleDeep Learning Approaches for Question Answering Systemen_US
dc.typeArticleen_US
Appears in Collections:Department of Computer Science and Information Systems

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