  We have constructed a new "Who-did-What" dataset of over 200,000
fill-in-the-gap (cloze) multiple choice reading comprehension problems
constructed from the LDC English Gigaword newswire corpus. The WDW dataset has
a variety of novel features. First, in contrast with the CNN and Daily Mail
datasets (Hermann et al., 2015) we avoid using article summaries for question
formation. Instead, each problem is formed from two independent articles --- an
article given as the passage to be read and a separate article on the same
events used to form the question. Second, we avoid anonymization --- each
choice is a person named entity. Third, the problems have been filtered to
remove a fraction that are easily solved by simple baselines, while remaining
84% solvable by humans. We report performance benchmarks of standard systems
and propose the WDW dataset as a challenge task for the community.
