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Asking good questions is an essential ability for both human and machineintelligence. However, existing neural question generation approaches mainlyfocus on the short factoid type of answers. In this paper, we propose a neuralquestion generator, MixQG, to bridge this gap. We combine 9 question answeringdatasets with diverse answer types, including yes/no, multiple-choice,extractive, and abstractive answers, to train a single generative model. Weshow with empirical results that our model outperforms existing work in bothseen and unseen domains and can generate questions with different cognitivelevels when conditioned on different answer types. Our code is released andwell-integrated with the Huggingface library to facilitate various downstreamapplications.

MixQG: Neural Question Generation with Mixed Answer Types