Harvesting Paragraph-level Question-Answer Pairs from Wikipedia
Open accessAbstract
We study the task of generating from Wikipedia articles question-answer pairs that cover content beyond a single sentence.We propose a neural network approach that incorporates coreference knowledge via a novel gating mechanism.Compared to models that only take into account sentence-level information (Heilman and Smith, 2010; Du et al., 2017;Zhou et al., 2017), we find that the linguistic knowledge introduced by the coreference representation aids question generation significantly, producing models that outperform the current state-of-theart.We apply our system (composed of an answer span extraction system and the passage-level QG system) to the 10,000 top-ranking Wikipedia articles and create a corpus of over one million questionanswer pairs.We also provide a qualitative analysis for this large-scale generated corpus from Wikipedia.
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In [12, 94], three crowd-workers rate questions on a 1-to-5 scale for fluency and relevance.
“An approach in which three crowd-workers are used for rating questions based on a scale of 1 to 5 (5 being good) on two parameters fluency and relevance is employed in [12, 94].”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [94]
The [91] model uses a multi-source encoder and an attention-and-copy decoder, and was tested on CoQA against baselines and ablations.
“A multi-source encoder along with a decoder based on attention and copy mechanism is employed for this task.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [94]
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References 39 · Cited by 168
Research works printed but not linked: 2
- Michael Denkowski and Alon Lavie. 2014. Me- teor universal: Language specific translation eval- uation for any target language. In Proceed- ings of the Ninth Workshop on Statistical Machine Translation. Association for Computational Lin- guistics, Baltimore, Maryland, USA, pages 376- 380. http://www.aclweb.org/anthology/W14-3348.
- Terry Winograd. 1972. Understanding natural lan- guage. Cognitive psychology 3(1):1-191.
- Authors
- Du, X., & Cardie, C.
- Affiliations
- Cornell University (US)
- Corresponding author
- Cardie, Claire
- ORCID
- Cardie, Claire
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2018
- Pages
- 1907-1917
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2962977247
- Cited by
- 168
- References
- 39
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 8 · 2023: 20 · 2022: 31 · 2021: 32 · 2020: 41 · 2019: 30 · 2018: 1
- FWCI
- 10.35
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 generation, question-answer pairs, Wikipedia, coreference, gating mechanism
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