Difference between revisions of "Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia"
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| + | {{Infobox work | ||
| + | | title = Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia | ||
| + | | date = 2018 | ||
| + | | authors = [[Xinya Du]]<br />[[Claire Cardie]] | ||
| + | | link = http://www.degruyter.com/view/j/zfal.2018.68.issue-1/zfal-2018-0003/zfal-2018-0003.xml | ||
| + | | plink = http://arxiv.org/pdf/1805.05942.pdf | ||
| + | }} | ||
'''Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Xinya Du]] and [[Claire Cardie]]. | '''Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Xinya Du]] and [[Claire Cardie]]. | ||
== Overview == | == Overview == | ||
Authors study the task of generating from [[Wikipedia]] articles question-answer pairs that cover content beyond a single sentence. Authors 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), authors find that the linguistic knowledge introduced by the coreference representation aids question generation significantly, producing models that outperform the current state-of-the-art. Authors apply 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. Authors also provide a qualitative analysis for this large-scale generated corpus from Wikipedia. | Authors study the task of generating from [[Wikipedia]] articles question-answer pairs that cover content beyond a single sentence. Authors 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), authors find that the linguistic knowledge introduced by the coreference representation aids question generation significantly, producing models that outperform the current state-of-the-art. Authors apply 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. Authors also provide a qualitative analysis for this large-scale generated corpus from Wikipedia. | ||
Revision as of 12:04, 10 May 2020
Authors | Xinya Du Claire Cardie |
|---|---|
Publication date | 2018 |
Links | Original Preprint |
Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia - scientific work related to Wikipedia quality published in 2018, written by Xinya Du and Claire Cardie.
Overview
Authors study the task of generating from Wikipedia articles question-answer pairs that cover content beyond a single sentence. Authors 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), authors find that the linguistic knowledge introduced by the coreference representation aids question generation significantly, producing models that outperform the current state-of-the-art. Authors apply 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. Authors also provide a qualitative analysis for this large-scale generated corpus from Wikipedia.
