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.- Anthology ID:
- 2021.emnlp-main.202
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2583–2593
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.202
- DOI:
- 10.18653/v1/2021.emnlp-main.202
- Cite (ACL):
- Fanyi Qu, Xin Jia, and Yunfang Wu. 2021. Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2583–2593, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data (Qu et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2021.emnlp-main.202.pdf
- Data
- RACE, SQuAD