Evaluating the Performance of Keras Implementation of MemNN Model for Simple and Complex Question Answering Tasks

Abstract

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. Memory networks are capable of reasoning with inference components combined with a long-term memory component and they learn how to use these two components in an efficient way to predict answers from the story text for a specific question.  This work intends to evaluate the performance of an earlier keras implementation of memory network (MemNN) model and compare its performance with three standard deep learning models RNN, LSTM and GRU.

In this work, we implement a Keras implementation of MemNN model based question answering systems and evaluate their performance with a simple and complex question answering tasks from bAbI dataset. We will study the performance of training and testing with suitable metrics and find the difference in performance in the two question answering tasks.