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Knowing the quality of reading comprehension (RC) datasets is important for the development of natural-language understanding systems. In this study, two classes of metrics were adopted for evaluating RC datasets: prerequisite skills and readability. We applied these classes to six existing datasets, including MCTest and SQuAD, and highlighted the characteristics of the datasets according to each metric and the correlation between the two classes. Our dataset analysis suggests that thedoi:10.18653/v1/p17-1075 dblp:conf/acl/SugawaraKYA17 fatcat:web46hzlv5fqdml2sjcub3uliq