Yusuke Kido


Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability
Saku Sugawara | Yusuke Kido | Hikaru Yokono | Akiko Aizawa
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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 the readability of RC datasets does not directly affect the question difficulty and that it is possible to create an RC dataset that is easy to read but difficult to answer.


Discourse Relation Sense Classification with Two-Step Classifiers
Yusuke Kido | Akiko Aizawa
Proceedings of the CoNLL-16 shared task