Asking questions like educational experts: automatically generating question-answer pairs on real-world examination data
Open accessAbstract
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.
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Transformer models can create distractor answers for multiple choice questionnaires.
“Beyond the generation of the correct answer, transformer models are also able to create distractor answers, as needed for the generation of multiple choice questionnaires [42, 43].”
From ChatGPT for good? On opportunities and challenges of large language models for education (2023) · cites this paper as [43]
Section 3.4 describes using ProphetNet to generate an answer, but does not say it generates distractor answers. A search for distractors found the term only in a cited work’s title in the References; our copy does not establish the claimed capability for transformer models. (LLM, conf=0.73)
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- Authors
- Qu, F., Jia, X., & Wu, Y.
- Affiliations
- Peking University (CN)
- Corresponding author
- Qu, Fanyi
- Venue
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Published
- 2021
- Pages
- 2583-2593
- OpenAlex
- W3199051863
- Cited by
- 30
- References
- 39
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 8 · 2023: 8 · 2022: 5
- FWCI
- 2.43
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- question-answer pair generation, keyphrase generation, educational question generation
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