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; , 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.
Recently, REF proposed a method that incorporated coreference knowledge into the neural networks to better encode this linguistically driven connection across entities for document-level question generation.
Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia
ArXiv
ArXiv
Computer Science