@InProceedings{buys-blunsom:2017:Long,
  author    = {Buys, Jan  and  Blunsom, Phil},
  title     = {Robust Incremental Neural Semantic Graph 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     = {1215--1226},
  abstract  = {Parsing sentences to linguistically-expressive semantic representations is a
	key goal of Natural Language Processing. Yet statistical parsing has focussed
	almost exclusively on bilexical dependencies or domain-specific logical forms.
	We propose a neural encoder-decoder transition-based parser which is the first
	full-coverage semantic graph parser for Minimal Recursion Semantics (MRS).
	The model architecture uses stack-based embedding features, predicting graphs
	jointly with unlexicalized predicates and their token alignments. Our parser
	is more accurate than attention-based baselines on MRS, and on an additional
	Abstract Meaning Representation (AMR) benchmark, and GPU batch processing
	makes it an order of magnitude faster than a high-precision grammar-based
	parser. Further, the 86.69% Smatch score of our MRS parser is higher than the
	upper-bound on AMR parsing, making MRS an attractive choice as a semantic
	representation.},
  url       = {http://aclweb.org/anthology/P17-1112}
}