Generating Logical Forms from Graph Representations of Text and Entities

ACL 2019 · Peter Shaw, Philip Massey, Angelica Chen, Francesco Piccinno, Yasemin Altun ·

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 state-of-the-art across several tasks without pre-training, and outperforms existing approaches when combined with BERT pre-training.

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Semantic Parsing

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Adam • Attention Dropout • BERT • Dense Connections • Dropout • GELU • Graph Neural Network • Layer Normalization • Linear Layer • Linear Warmup With Linear Decay • Multi-Head Attention • Residual Connection • Scaled Dot-Product Attention • Softmax • Weight Decay • WordPiece

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Generating Logical Forms from Graph Representations of Text and Entities

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