  Generating high quality question-answer pairs is a hard but meaningful task.
Although previous works have achieved great results on answer-aware question
generation, it is difficult to apply them into practical application in the
education field. This paper for the first time addresses the question-answer
pair generation task on the real-world examination data, and proposes a new
unified framework on RACE. To capture the important information of the input
passage we first automatically generate(rather than extracting) keyphrases,
thus this task is reduced to keyphrase-question-answer triplet joint
generation. Accordingly, we propose a multi-agent communication model to
generate and optimize the question and keyphrases iteratively, and then apply
the generated question and keyphrases to guide the generation of answers. To
establish a solid benchmark, we build our model on the strong generative
pre-training model. Experimental results show that our model makes great
breakthroughs in the question-answer pair generation task. Moreover, we make a
comprehensive analysis on our model, suggesting new directions for this
challenging task.
