@InProceedings{cheng-EtAl:2017:Long,
  author    = {Cheng, Jianpeng  and  Reddy, Siva  and  Saraswat, Vijay  and  Lapata, Mirella},
  title     = {Learning Structured Natural Language Representations for Semantic Parsing},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {44--55},
  abstract  = {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 dif- ferent from
	linguistically motivated ones.},
  url       = {http://aclweb.org/anthology/P17-1005}
}