@InProceedings{dozat-qi-manning:2017:K17-3,
  author    = {Dozat, Timothy  and  Qi, Peng  and  Manning, Christopher D.},
  title     = {Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task},
  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     = {20--30},
  abstract  = {This paper describes the neural dependency parser submitted by Stanford to the
	CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses
	relatively simple LSTM networks to produce part of speech tags and labeled
	dependency parses from segmented and tokenized sequences of words. In order to
	address the rare word problem that abounds in languages with complex
	morphology, we include a character-based word representation that uses an LSTM
	to produce embeddings from sequences of characters. Our system was ranked first
	according to all five relevant metrics for the system: UPOS tagging (93.09%),
	XPOS tagging (82.27%), unlabeled attachment score (81.30%), labeled attachment
	score (76.30%), and content word labeled attachment score (72.57%).},
  url       = {http://www.aclweb.org/anthology/K17-3002}
}