Generating Logical Forms from Graph Representations of Text and Entities
Peter Shaw, Philip Massey, Angelica Chen, Francesco Piccinno, Yasemin, Altun

TL;DR
This paper introduces a GNN-based method for generating logical forms from text and entity graphs, effectively incorporating entity relations and achieving competitive results without pre-training, and superior performance with BERT.
Contribution
The paper presents a novel GNN architecture combined with a decoder copy mechanism for logical form generation from graph representations, improving over prior methods.
Findings
Competitive with state-of-the-art without pre-training
Outperforms existing approaches with BERT pre-training
Effective incorporation of entity relations in logical form generation
Abstract
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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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Advanced Graph Neural Networks
MethodsGraph Neural Network · Linear Layer · Residual Connection · Attention Dropout · Linear Warmup With Linear Decay · Weight Decay · Refunds@Expedia|||How do I get a full refund from Expedia? · Dense Connections · Adam · WordPiece
