%0 Conference Proceedings %T Harvesting Paragraph-level Question-Answer Pairs from Wikipedia %A Du, Xinya %A Cardie, Claire %Y Gurevych, Iryna %Y Miyao, Yusuke %S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) %D 2018 %8 July %I Association for Computational Linguistics %C Melbourne, Australia %F du-cardie-2018-harvesting %X 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. %R 10.18653/v1/P18-1177 %U https://aclanthology.org/P18-1177 %U https://doi.org/10.18653/v1/P18-1177 %P 1907-1917