@InProceedings{vilares-gomezrodriguez:2017:K17-3,
  author    = {Vilares, David  and  G\'{o}mez-Rodr\'{i}guez, Carlos},
  title     = {A non-projective greedy dependency parser with bidirectional LSTMs},
  booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {152--162},
  abstract  = {The LyS-FASTPARSE team present BIST-COVINGTON, a neural implementation of the
	Covington (2001) algorithm for non-projective dependency parsing. The 
	bidirectional LSTM approach by Kiperwasser and Goldberg (2016) is used to train
	a greedy parser with a dynamic oracle to mitigate error propagation. The model
	participated in the CoNLL 2017 UD Shared Task. In spite of not using any
	ensemble methods and using the baseline segmentation and PoS tagging, the
	parser obtained good results on both macro-average LAS and UAS in the big
	treebanks category (55 languages), ranking 7th out of 33 teams. In the all
	treebanks category (LAS and UAS) we ranked 16th and 12th. The gap between the
	all and big categories is mainly due to the poor performance on four parallel
	PUD treebanks, suggesting that some 'suffixed' treebanks (e.g. Spanish-AnCora)
	perform poorly on cross-treebank settings, which does not occur with the
	corresponding 'unsuffixed' treebank  (e.g. Spanish). By changing that, we
	obtain the 11th best LAS among all runs (official and unofficial). The code is
	made available at https://github.com/CoNLL-UD-2017/LyS-FASTPARSE},
  url       = {http://www.aclweb.org/anthology/K17-3016}
}