@InProceedings{che-EtAl:2017:K17-3,
  author    = {Che, Wanxiang  and  Guo, Jiang  and  Wang, Yuxuan  and  Zheng, Bo  and  Zhao, Huaipeng  and  Liu, Yang  and  Teng, Dechuan  and  Liu, Ting},
  title     = {The HIT-SCIR System for End-to-End Parsing of Universal Dependencies},
  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     = {52--62},
  abstract  = {This paper describes our system (HIT-SCIR) for the CoNLL 2017 shared task:
	Multilingual Parsing from Raw Text to Universal Dependencies.
	Our system includes three pipelined components: \textit{tokenization},
	\textit{Part-of-Speech} (POS) \textit{tagging} and \textit{dependency parsing}.
	We use character-based bidirectional long short-term memory (LSTM) networks for
	both tokenization and POS tagging.
	Afterwards, we employ a list-based transition-based algorithm for general
	non-projective parsing and present an improved Stack-LSTM-based architecture
	for representing each transition state and making predictions.
	Furthermore, to parse low/zero-resource languages and cross-domain data, we use
	a model transfer approach to make effective use of existing resources.
	We demonstrate substantial gains against the UDPipe baseline, with an average
	improvement of 3.76\% in LAS of all languages. And finally, we rank the 4th
	place on the official test sets.},
  url       = {http://www.aclweb.org/anthology/K17-3005}
}