Evaluating the Performance of Recurrent Neural Network based Question Answering System with Easy and Complex bAbI QA Tasks
Question Answering (QA) system is a field of Natural language processing, which allows users to ask questions using the natural language sentence and return a brief answer to the users rather than a list of documents. This work intends to use a Recurrent Neural Network (RNN) based Deep Learning algorithms in order to solve the Question Answering System problem. The use of recurrent neural networks allows us to expand and apply this model to a variety of question answering tasks. In this work, a simple RNN based Question Answering System is implemented and its performance is evaluated with a simple and complex question answering tasks using bAbI dataset. The performance of training and testing with suitable metrics is studied and the difference in performance in the two question answering tasks is observed.