A challenge in creating a dataset for machine reading comprehension (MRC) is to collect
questions that require a sophisticated understanding of language to answer beyond using …

Existing analysis work in machine reading comprehension (MRC) is largely concerned with
evaluating the capabilities of systems. However, the capabilities of datasets are not …

Machine reading comprehension (MRC) aims to teach machines to read and comprehend
human languages, which is a long-standing goal of natural language processing (NLP) …

We present a new dataset for machine comprehension in the medical domain. Our dataset
uses clinical case reports with around 100,000 gap-filling queries about these cases. We …

This paper describes the methodology for testing the performance of Machine Reading
systems through Question Answering and Reading Comprehension Tests. This was the …

Abstract Evaluation campaigns of Question Answering (QA) systems have contributed to the
development of such technologies. These campaigns have promoted some changes …

Knowing the quality of reading comprehension (RC) datasets is important for the
development of natural-language understanding systems. In this study, two classes of …

Question-answering (QA) systems aim to provide answers for given questions. The answers
can be extracted or generated from either unstructured or structured text. Therefore, QA is …

Machine reading comprehension (MRC) has received considerable attention as a
benchmark for natural language understanding. However, the conventional task design of …

In response to the CLEF eRisk 2019 shared task on measuring the severity of the signs of
depression from threads of user submissions on social media, our team has developed a …