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This paper makes one of the first efforts toward automatically generatingcomplex questions from knowledge graphs. Particularly, we study how to leverageexisting simple question datasets for this task, under two separate scenarios:using either sub-questions of the target complex questions, or distantlyrelated pseudo sub-questions when the former are unavailable. First, acompetitive base model named CoG2Q is designed to map complex query qraphs tonatural language questions. Afterwards, we propose two extension models, namelyCoGSub2Q and CoGSub^m2Q, respectively for the above two scenarios. The formerencodes and copies from a sub-question, while the latter further scores andaggregates multiple pseudo sub-questions. Experiment results show that theextension models significantly outperform not only base CoG2Q, but also itsaugmented variant using simple questions as additional training examples. Thisdemonstrates the importance of instance-level connections between simple andcorresponding complex questions, which may be underexploited by straightforwarddata augmentation of CoG2Q that builds model-level connections through learnedparameters.

Easy-to-Hard: Leveraging Simple Questions for Complex Question Generation