Past research took a reductionist approach, separately considering these two problems of “what” and “how” via content selection and question construction.  In contrast, neural models motivate an end-to-end architectures. Deep learned frameworks contrast with the reductionist approach, admitting approaches that jointly optimize for both the “what” and “how” in an unified framework.  
 Question: What learning paradigms do they cover in this survey?
Considering "What" and "How" separately versus jointly optimizing for both.