mlsanityData to 7 Oct 2026

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

arXiv 1905.10044 ↗First posted 24 May 2019cs.CLcited by 2,140

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Cited by 176 in Mixture-of-experts, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this paper we study yes/no questions that are naturally occurring --- meaning that they are generated in unprompted and unconstrained settings. We build a reading comprehension dataset, BoolQ, of such questions, and show that they are unexpectedly challenging. They often query for complex, non-factoid information, and require difficult entailment-like inference to solve. We also explore the effectiveness of a range of transfer learning baselines. We find that transferring from entailment data is more effective than transferring from paraphrase or extractive QA data, and that it, surprisingly, continues to be very beneficial even when starting from massive pre-trained language models such as BERT. Our best method trains BERT on MultiNLI and then re-trains it on our train set. It achieves 80.4% accuracy compared to 90% accuracy of human annotators (and 62% majority-baseline), leaving a significant gap for future work.

Key facts. Cited by 2,140 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2019.

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