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Key-Value Memory Networks for Directly Reading Documents

Introduction

• Knowledge Bases (KBs) are effective tools for Question Answering (QA) but are often too restrictive (due to fixed schema) and too sparse (due to limitations of Information Extraction (IE) systems).
• The paper proposes Key-Value Memory Networks, a neural network architecture based on Memory Networks that can leverage both KBs and raw data for QA.
• The paper also introduces MOVIEQA, a new QA dataset that can be answered by a perfect KB, by Wikipedia pages and by an imperfect KB obtained using IE techniques thereby allowing a comparison between systems using any of the three sources.
• Link to the paper.

Related Work

chiwakii

#include <iostream>
#include <sstream>

#include <Windows.h>
#include <Kinect.h>
#include <opencv2/opencv.hpp>

#include <atlbase.h>

// Error Check 

chiwakii

// -------------------------------------------------------------------------
// Filename:	astar.h
// Version:	1.24 
// Date:	2002/03/08
// Purpose:	Provide template for a* algorythm
// (c) T.Frogley 1999-2002
// -------------------------------------------------------------------------

#ifndef ASTAR_H
#define ASTAR_H