Open AccessAnalysis on the Language Independent and Dependent Aspects of Deep Learning based Question Answering Systems
Author(s) -
R Poonguzhali,
K. Mohana Lakshmi
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f4816.049620
Subject(s) - question answering , computer science , scope (computer science) , natural language processing , artificial intelligence , natural language , field (mathematics) , language identification , natural language programming , universal networking language , natural (archaeology) , language model , comprehension approach , programming language , mathematics , archaeology , pure mathematics , history
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