To address the answer generation problem, we propose a novel KEAG model which is able to compose a natural answer by recurrently selecting words at the decoding stage.
each of the words comes from one of the four sources: question q, passage p, global vocabulary V, and knowledge K. at every generation step, KeAG first determines which of the four sources to inspect based on the current state, and then generates a new word from the chosen source to make up a final answer.