Memory Networks for Q&A(Question and Answer) Applications
This article was published as a part of the Data Science Blogathon
Introduction
This article shows the power of Memory Networks for Question and Answer (QA) applications in the context of simple natural language-based reasoning.
Table of Contents
- What is the motivation behind Memory Networks?
- Why do we need Memory Networks when traditional NLP models are already performing well?
- Facebook bAbI dataset
- About Supporting Fact
- Components of Memory Networks
- How can we find the best match?
- How does the dot product find the matching?
- Sample QA application
- Endnotes
What is the motivation behind Memory Networks?
The basic motivation for Memory Networks is an attempt to add Long-term memory to save the knowledge of the question and answer. So external memory is used as a knowledge base to make QA applications like artificial intelligence.
Why do we need Memory Networks when traditional NLP models are already performing well?
It is important to store a large amount of prior knowledge for reasoning. Traditional deep learning