Yiğit, GülsümYiğit, GülsümAmasyalı, Mehmet Fatih2020-12-242020-12-2420191978-172812868-9https://hdl.handle.net/20.500.12469/3651https://doi.org/10.1109/ASYU48272.2019.8946411Most 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.eninfo:eu-repo/semantics/closedAccessDynamic Memory NetworkNatural Language ProcessingQuestion AnsweringConference Object10/01/19WOS:00063125240002410.1109/ASYU48272.2019.89464112-s2.0-85078332209N/AN/A