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dc.contributor.authorYiğit, Gülsüm
dc.contributor.authorAmasyalı, Mehmet Fatih
dc.description.abstractMost of the natural language processing problems can be reduced into a question answering problem. Dynamic Memory Networks (DMNs) are one of the solution approaches for question answering problems. Based on the analysis of a question answering system built by DMNs described in [1], this study proposes a model named DMN∗ which contains several improvements on its input and attention modules. DMN∗ architecture is distinguished by a multi-layer bidirectional LSTM (Long Short Term Memory) architecture on input module and several changes in computation of attention score in attention module. Experiments are conducted on Facebook bAbi dataset [2]. We also introduce Turkish bAbi dataset, and produce increased vocabulary sized tasks for each dataset. The experiments are performed on English and Turkish datasets and the accuracy performance results are compared by the work described in [1]. Our evaluation shows that the proposed model DMN∗ obtains improved accuracy performance results on various tasks for both Turkish and English.en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectDynamic Memory Networken_US
dc.subjectNatural Language Processingen_US
dc.subjectQuestion Answeringen_US
dc.titleAsk me: A Question Answering System via Dynamic Memory Networksen_US
dc.typeConference Paperen_US
dc.relation.journalProceedings - 2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019en_US
dc.identifier.volumeOctober 2019en_US
dc.contributor.khasauthorYiğit, Gülsümen_US

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