Difference between revisions of "Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia"
(Starting a page: Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia) |
(Links) |
||
| Line 1: | Line 1: | ||
| − | '''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 08:48, 19 January 2020
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.
