Difference between revisions of "Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia"
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− | '''Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia''' - scientific work related to Wikipedia quality published in 2018, written by Xinya Du and Claire Cardie. | + | {{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]]. | ||
== 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. |
+ | |||
+ | == Embed == | ||
+ | === Wikipedia Quality === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Du, Xinya; Cardie, Claire. (2018). "[[Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia]]". | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === English Wikipedia === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | {{cite journal |last1=Du |first1=Xinya |last2=Cardie |first2=Claire |title=Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia |date=2018 |url=https://wikipediaquality.com/wiki/Harvesting_Paragraph-Level_Question-Answer_Pairs_from_Wikipedia}} | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === HTML === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Du, Xinya; Cardie, Claire. (2018). &quot;<a href="https://wikipediaquality.com/wiki/Harvesting_Paragraph-Level_Question-Answer_Pairs_from_Wikipedia">Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia</a>&quot;. | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | |||
+ | |||
+ | [[Category:Scientific works]] |
Latest revision as of 09:25, 18 February 2021
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.
Embed
Wikipedia Quality
Du, Xinya; Cardie, Claire. (2018). "[[Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia]]".
English Wikipedia
{{cite journal |last1=Du |first1=Xinya |last2=Cardie |first2=Claire |title=Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia |date=2018 |url=https://wikipediaquality.com/wiki/Harvesting_Paragraph-Level_Question-Answer_Pairs_from_Wikipedia}}
HTML
Du, Xinya; Cardie, Claire. (2018). "<a href="https://wikipediaquality.com/wiki/Harvesting_Paragraph-Level_Question-Answer_Pairs_from_Wikipedia">Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia</a>".