Analysis on the Language Independent and Dependent Aspects of Deep Learning based Question Answering Systems
R. Poonguzhali1, K. Lakshmi2
1R. Poonguzhali*, Computer Science and Engineering, Periyar Maniammai Institute of Science and Technology (Deemed to be University), Thanjavur, India.
2K. Lakshmi, Computer Science and Engineering, Periyar Maniammai Institute of Science and Technology (Deemed to be University), Thanjavur, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 26, 2020. | Manuscript published on April 10, 2020. | PP: 2057-2062 | Volume-9 Issue-6, April 2020. | Retrieval Number: F4816049620/2020©BEIESP | DOI: 10.35940/ijitee.F3308.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Natural languages are ambiguous and computers are not capable of understanding the natural languages in the way people really understand them. Natural Language Processing (NLP) is concerned with the development of computational models based on the aspects of human language processing. Question Answering (QA) system is a field of Natural Language Processing that provides precise answer for the user question which is given in natural language. In this work, a MemN2N model based question answering system is implemented and its performance is evaluated with a complex question answering tasks using bAbI dataset of three different language text corpuses. The scope of this work is to understand the language independent and dependant aspects of a deep learning network. For this, we will study the performance of the deep learning network by training and testing it with different kinds of question answering tasks with different languages and also try to understand the difference in performance with respect to the languages
Keywords: NLP, QA, Deep learning, MemNN, Memory Networks, MemN2N, End to End Memory Networks, RNN, LSTM, GRU, bAbI Tasks.
Scope of the Article: Deep learning