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Structured information about entities is critical for many semantic parsing tasks. We present an approach that uses a Graph Neural Network (GNN) architecture to incorporate information about relevant entities and their relations during parsing. Combined with a decoder copy mechanism, this approach provides a conceptually simple mechanism to generate logical forms with entities. We demonstrate that this approach is competitive with the stateof-the-art across several tasks without pretraining,doi:10.18653/v1/p19-1010 dblp:conf/acl/ShawMCPA19 fatcat:nd2ayfiqcnhu7pbcsbnnvlrbcq