Learning Structured Natural Language Representations for Semantic Parsing
arXiv:1704.08387
Abstract
We introduce a neural semantic parser that converts natural language utterances to intermediate representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We obtain competitive results on various datasets. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones.
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- Dependency-based Hybrid Trees for Semantic Parsing
- Learning an Executable Neural Semantic Parser
- Transferable Natural Language Interface to Structured Queries aided by Adversarial Generation
- Question Answering via Web Extracted Tables and Pipelined Models
- Pragmatic approach to structured data querying via natural language interface
- A survey of cross-lingual features for zero-shot cross-lingual semantic parsing