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Most research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. We introduce a neural model which integrates and reasons relying on information spread within documents and across multiple documents. We frame it as an inference problem on a graph. Mentions of entities are nodes of this graph while edges encode relations between different mentions (e.g., within-and crossdocument coreference). Graph convolutional networks (GCNs)doi:10.18653/v1/n19-1240 dblp:conf/naacl/CaoAT19 fatcat:7jdqxkfhgnetdcc5mjtlmji4xy