@InProceedings{wang-zhao-zhang:2017:K17-3,
  author    = {Wang, Hao  and  Zhao, Hai  and  Zhang, Zhisong},
  title     = {A Transition-based System for Universal Dependency Parsing},
  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     = {191--197},
  abstract  = {This paper describes the system for our participation in the CoNLL 2017 Shared
	Task: Multilingual Parsing from Raw Text to Universal Dependencies. In this
	work, we design a system based on UDPipe1 for universal dependency parsing,
	where multilingual transition-based models are trained for different treebanks.
	Our system directly takes raw texts as input, performing several intermediate
	steps like tokenizing and tagging, and finally generates the corresponding
	dependency
	trees. For the special surprise languages for this task, we adopt a
	delexicalized strategy and predict basing on transfer learning from other
	related languages. In the final evaluation of the shared task, our system
	achieves a result of 66.53% in macro-averaged LAS F1-score.},
  url       = {http://www.aclweb.org/anthology/K17-3020}
}