We introduce a neural semantic parser which 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 achieve the state of the art on SPADES and GRAPHQUESTIONS and obtain competitive results on GEOQUERY and WEB-QUESTIONS. The induced predicateargument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones. 1
Cheng et al. REF introduce a neural semantic parser that uses predicate-argument structures to convert natural language utterances to intermediate representations.
Learning Structured Natural Language Representations for Semantic Parsing
ArXiv
ArXiv
Computer Science