Proceedings of the Annual Meeting of the Cognitive Science Society

Asking creative questions is a hallmark of human cognition. In comparison, machine learning systems that attemptto mimic this ability are still extremely limited (e.g., current chatbots ask questions based on preprogrammed routines). In thepresent work, we developed a computational model of question generation. Based on a corpus of questions collected from onlineparticipants playing an information-seeking game, we designed a “grammar of questions.” The grammar is powerful enough torepresent all human questions we collected and thus defines the “question space.” Given a particular context (game scenario),people are more likely to ask (generate) some questions that others. Our computational model predicts these likelihoods, thatis, a probability distribution over the question space. In addition, the model can generalize to novel contexts. Key modelingredients are informativity, compositionality, and length of a question.