Abstract
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. As 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-the-art. 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 question-answer pairs. We provide qualitative analysis for the this large-scale generated corpus from Wikipedia.- Anthology ID:
- P18-1177
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1907–1917
- Language:
- URL:
- https://aclanthology.org/P18-1177
- DOI:
- 10.18653/v1/P18-1177
- Cite (ACL):
- Xinya Du and Claire Cardie. 2018. Harvesting Paragraph-level Question-Answer Pairs from Wikipedia. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1907–1917, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Harvesting Paragraph-level Question-Answer Pairs from Wikipedia (Du & Cardie, ACL 2018)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/P18-1177.pdf
- Code
- xinyadu/harvestingQA
- Data
- SQuAD, SimpleQuestions, WebQuestions