Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

Machine Reading Comprehension (MRC) is a challenging Natural Language Processing
(NLP) research field with wide real-world applications. The great progress of this field in …

We present the Stanford Question Answering Dataset (SQuAD), a new reading
comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set …

We present NewsQA, a challenging machine comprehension dataset of over 100,000
human-generated question-answer pairs. Crowdworkers supply questions and answers …

Enabling a computer to understand a document so that it can answer comprehension
questions is a central, yet unsolved goal of NLP. A key factor impeding its solution by …

Several large cloze-style context-question-answer datasets have been introduced recently:
the CNN and Daily Mail news data and the Children's Book Test. Thanks to the size of these …

Teaching machines to understand human language documents is one of the most elusive
and long-standing challenges in Artificial Intelligence. This thesis tackles the problem of …

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 …

Previous machine comprehension (MC) datasets are either too small to train end-to-end
deep learning models, or not difficult enough to evaluate the ability of current MC …

Building curious machines that can answer as well as ask questions is an important
challenge for AI. The two tasks of question answering and question generation are usually …