This observation motivates us to approach the reading comprehension problem in a constituentcentric manner, where the generation of candidate answers and their representation learning are both based upon constituents.
we design a Constituent-Centric Neural Network (CCNN) to perform end-to-end reading comprehension, where the inputs are the passage and question, and the output is a span in the passage that is mostly suitable to answer this question.