Introduction BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally occurring ---they are generated in unprompted and unconstrained settings. Content Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. The text-pair classification setup is similar to existing natural language inference tasks. By sampling questions from a distribution of information-seeking queries (rather than prompting annotators for text pairs), we observe significantly more challenging examples compared to existing NLI datasets. Details More details are available in our paper, which can be cited as follows. @inproceedings{clark2019boolq, title = {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions}, author = {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina}, booktitle = {NAACL}, year = {2019}, }