Harvesting Paragraph-level Question-Answer Pairs from Wikipedia
Xinya Du, Claire Cardie · 2018
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.