@InProceedings{vania-zhang-lopez:2017:K17-3,
  author    = {Vania, Clara  and  Zhang, Xingxing  and  Lopez, Adam},
  title     = {UParse: the Edinburgh system for the CoNLL 2017 UD 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     = {100--110},
  abstract  = {This paper presents our submissions for the CoNLL 2017 UD Shared Task. Our
	parser, called UParse, is based on a neural network graph-based dependency
	parser. The parser uses features from a bidirectional LSTM to to produce a
	distribution over possible heads for each word in the sentence. To allow
	transfer learning for low-resource treebanks and surprise languages, we train
	several multilingual models for related languages, grouped by their genus and
	language families. Out of 33 participants, our system achieves rank 9th in the
	main results, with 75.49 UAS and 68.87 LAS F-1 scores (average across 81
	treebanks).},
  url       = {http://www.aclweb.org/anthology/K17-3010}
}