  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.
