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Generating Logical Forms from Graph Representations of Text and Entities

Philip Massey
Angie Chen
ACL 2019 (2019) (to appear)


Semantic parsing maps natural language utterances into structured meaning representations. 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 also provides a conceptually simple mechanism to generate logical forms with entities. We demonstrate that this approach is competitive with state-of-the-art across several tasks without pre-training, and outperforms existing approaches when combined with BERT pre-training.