@InProceedings{stanojevic-garridoalhama:2017:EMNLP2017,
  author    = {Stanojevi\'{c}, Milo\v{s}  and  Garrido Alhama, Raquel},
  title     = {Neural Discontinuous Constituency Parsing},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1666--1676},
  abstract  = {One of the most pressing issues in discontinuous constituency transition-based
	parsing is that the relevant information for parsing decisions could be located
	in any part of the stack or the buffer. 
	In this paper, we propose a solution to this problem by replacing the
	structured perceptron model with a recursive neural model that computes a
	global representation of the configuration, therefore allowing even the most
	remote parts of the configuration to influence the parsing decisions. We also
	provide a detailed analysis of how this representation should be built out of
	sub-representations of its core elements (words, trees and stack).
	 Additionally, we investigate how different types of swap oracles influence the
	results.  Our model is the first neural discontinuous constituency parser, and
	it outperforms all the previously published models on three out of four
	datasets while on the fourth it obtains second place by a tiny difference.},
  url       = {https://www.aclweb.org/anthology/D17-1174}
}