@inproceedings{qu-etal-2021-asking,
title = "Asking Questions Like Educational Experts: {A}utomatically Generating Question-Answer Pairs on Real-World Examination Data",
author = "Qu, Fanyi and
Jia, Xin and
Wu, Yunfang",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2021.emnlp-main.202/",
doi = "10.18653/v1/2021.emnlp-main.202",
pages = "2583--2593",
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."
}
Markdown (Informal)
[Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data](https://preview.aclanthology.org/Author-page-Marten-During-lu/2021.emnlp-main.202/) (Qu et al., EMNLP 2021)
ACL