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

Abstract

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.

Accepted as a long paper in the main conference of EMNLP 2021

References in corpus (6)

• Natural Language Processing (almost) from Scratch
• Question Answering and Question Generation as Dual Tasks
• A Joint Model for Question Answering and Question Generation
• Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model
• OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach
• EQG-RACE: Examination-Type Question Generation