Generating Logical Forms from Graph Representations of Text and Entities
arXiv:1905.08407
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.
ACL 2019
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- ETC: Encoding Long and Structured Inputs in Transformers
- Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base
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- ShopTalk: A System for Conversational Faceted Search