Asking questions is a pervasive human activity, but little is understood about what makes them difficult to answer. An analysis of a pair of large databases, New York Times crosswords and questions from the quiz-show Jeopardy, establishes two orthogonal dimensions of question difficulty: obscurity (the rarity of the answer) and opacity (the indirectness of question cues, operationalized with word2vec). The importance of opacity, and the role of synergistic information in resolving it, suggests that accounts of difficulty in terms of prior expectations captures only a part of the question-asking process. A further regression analysis shows the presence of additional dimensions to question-asking: question complexity, the answer's local network density, cue intersection, and the presence of signal words. Our work shows how question-askers can help their interlocutors by using contextual cues, or, conversely, how a particular kind of unfamiliarity with the domain in question can make it harder for individuals to learn from others. Taken together, these results suggest how Bayesian models of question difficulty can be supplemented by process models and accounts of the heuristics individuals use to navigate conceptual spaces.

中文翻译：

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不透明性，模糊性和提问的方式。

提出问题是人类的普遍活动，但对于使他们难以回答的原因却知之甚少。对一对大型数据库，《纽约时报》填字游戏和测验节目Jeopardy中的问题的分析确定了问题难度的两个正交维度：晦涩（答案的稀有性）和不透明性（问题提示的间接性，已通过word2vec进行了操作） ）。不透明的重要性以及协同信息在解决中的作用表明，根据先期期望来解决困难仅占提问过程的一部分。进一步的回归分析显示了提出问题的其他维度：问题复杂性，答案的本地网络密度，提示交点和信号词的存在。我们的工作表明，提问者如何通过使用上下文提示来帮助对话者，或者相反，特定的对所涉领域的不熟悉如何使个人更难以向他人学习。综上所述，这些结果表明问题处理的贝叶斯模型如何可以通过过程模型和个人用于导航概念空间的启发式方法加以补充。