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In this paper we study yes/no questions that are naturally occurring ---meaning that they are generated in unprompted and unconstrained settings. Webuild a reading comprehension dataset, BoolQ, of such questions, and show thatthey are unexpectedly challenging. They often query for complex, non-factoidinformation, and require difficult entailment-like inference to solve. We alsoexplore the effectiveness of a range of transfer learning baselines. We findthat transferring from entailment data is more effective than transferring fromparaphrase or extractive QA data, and that it, surprisingly, continues to bevery beneficial even when starting from massive pre-trained language modelssuch as BERT. Our best method trains BERT on MultiNLI and then re-trains it onour train set. It achieves 80.4% accuracy compared to 90% accuracy of humanannotators (and 62% majority-baseline), leaving a significant gap for futurework.

BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions