Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data

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.