Thus, our research studies methods for incorporating coreference information into the training of a question generation system.
we propose gated Coreference knowledge for Neural Question Generation (CorefNQG), a neural sequence model with a novel gating mechanism that leverages continuous representations of coreference clusters -the set of mentions used to refer to each entity -to better encode linguistic knowledge introduced by coreference, for paragraph-level question generation.