Review
Version 1
This version is not peer-reviewed
Datasets for Machine Reading Comprehension: A Literature Review
Version 1
: Received: 5 October 2019 / Approved: 8 October 2019 / Online: 8 October 2019 (10:40:09 CEST)
A peer-reviewed article of this Preprint also exists.
Abstract
Machine reading comprehension aims to teach machines to understand a text like a human, and is a new challenging direction in Artificial Intelligence. Datasets play an important role while describing or building an algorithm for machine reading comprehension. The type of answers we required from developed algorithm depends on datasets.The datasets are classified into two types, namely datasets with extractive answers and datasets with descriptive answers. This article summarize both datasets with an example of each type to get better insight of datasets in machine reading comprehension and which datasets to use depending the requirements.
Keywords
Reading Comprehensio; Descriptive Answer; Extractive answer; Multiple-choice answers; Datasets
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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