@InProceedings{corro-leroux-lacroix:2017:EMNLP2017,
  author    = {Corro, Caio  and  Le Roux, Joseph  and  Lacroix, Mathieu},
  title     = {Efficient Discontinuous Phrase-Structure Parsing via the Generalized Maximum Spanning Arborescence},
  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     = {1644--1654},
  abstract  = {We present a new method for the joint task of tagging and non-projective
	dependency parsing. We demonstrate its usefulness with an application to
	discontinuous phrase-structure parsing where decoding lexicalized spines and
	syntactic derivations is performed jointly. The main contributions of this
	paper are (1) a reduction from joint tagging and non-projective dependency
	parsing to the Generalized Maximum Spanning Arborescence problem, and (2) a
	novel decoding algorithm for this problem through Lagrangian relaxation. We
	evaluate this model and obtain state-of-the-art results despite strong
	independence assumptions.},
  url       = {https://www.aclweb.org/anthology/D17-1172}
}