# Who did What: A Large-Scale Person-Centered Cloze Dataset

arXiv: https://arxiv.org/abs/1608.05457 · HTML: https://mlsanity.com/paper/1608.05457/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2016.

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- [InDEX: Indonesian Idiom and Expression Dataset for Cloze Test](https://mlsanity.com/paper/2211.13376/) (2022)
- [Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets](https://mlsanity.com/paper/2408.02114/) (2024)

## Abstract (arXiv)

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.