In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

[EX Q]: RKS approach proposed in BIBREF21, BIBREF22, explicitly maps data vectors to a space where linear separation is possible. The RKS method provides an approximate kernel function via explicit mapping. 
 Question: What is the Random Kitchen Sink approach?
[EX A]: Random Kitchen Sink method uses a kernel function to map data vectors to a space where linear separation is possible.

[EX Q]: (2) Different from LEM and DPEMM, AEM uses a generator network to capture the event-related patterns and is able to mine events from different text sources (short and long). Moreover, unlike traditional inference procedure, such as Gibbs sampling used in LEM and DPEMM, AEM could extract the events more efficiently due to the CUDA acceleration; 
 Question: What alternative to Gibbs sampling is used?
[EX A]: generator network to capture the event-related patterns

[EX Q]: 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?
[EX A]:
Considering "What" and "How" separately versus jointly optimizing for both.