Learning Structured Natural Language Representations for Semantic Parsing
Jianpeng Cheng, Siva Reddy, Vijay Saraswat, Mirella Lapata · 2017
We introduce a neural semantic parser which is interpretable and scalable.Our model converts natural language utterances to intermediate, domain-general natural language 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 GEO-QUERY and WEBQUESTIONS.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. 1