Asking Questions Like Educational Experts: A utomatically Generating Question-Answer Pairs on Real-World Examination Data (2021)

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.

## introduction question-answer pair generation (qag) is to do question generation (qg) and answer generation (ag) simultaneously only with a given passage. the generated question-answer (q-a) pairs can be effectively applied in numbers of tasks such as knowledge management @xcite , faq document generation @xcite , and data enhancement for reading comprehension tasks @xcite . particularly, high-quality q-a pairs can facilitate the instructing process and benefit on creating educational materials for reading practice and assessment @xcite ). recently, much work has devoted to qg, while the qag task is less addressed. the existing approaches on this task can be roughly grouped into two categories: the joint learning method to treat qg and answer extraction as dual tasks @xcite ; the pipeline strategy that considers answer extraction and qg as two sequential processes @xcite . ## data analysis in this section, we present a deep dive into squad and race to explore the challenging of qag on real-world examination data. ## conclusion in this paper, we address the qag task and propose a unified framework trained on the educational dataset race. we adopt a three-stage generation method with a rough keyphrase generation model, an iterative message-passing module and a question-keyphrase guided answer generation model. our model achieves close performance as the state-of-the-art answer-aware generation model on qg task, and obtains a great improvement on the answerability of the generated pairs compared to the basic pre-training model. there is significant potential for further improvement in our proposed qag task, to help people produce reading comprehension data in real-world applications.