Who did What: A Large Scale Person Centered Cloze Dataset
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.
EMNLP