P17-1005@ACL

Total: 1

#1 Learning Structured Natural Language Representations for Semantic Parsing [PDF] [Copy] [Kimi1] [REL]

Authors: Jianpeng Cheng, Siva Reddy, Vijay Saraswat, Mirella Lapata

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 GEOQUERY 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.