@InProceedings{guo-EtAl:2016:COLING1,
  author    = {Guo, Jiang  and  Che, Wanxiang  and  Wang, Haifeng  and  Liu, Ting},
  title     = {A Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {12--22},
  abstract  = {Various treebanks have been released for dependency parsing.
	Despite that treebanks may belong to different languages or have different
	annotation schemes, they contain common syntactic knowledge that is potential
	to benefit each other.
	This paper presents a universal framework for transfer parsing across
	multi-typed treebanks with deep multi-task learning.
	We consider two kinds of treebanks as source: the multilingual universal
	treebanks and the monolingual heterogeneous treebanks.
	Knowledge across the source and target treebanks are effectively transferred
	through multi-level parameter sharing.
	Experiments on several benchmark datasets in various languages demonstrate that
	our approach can make effective use of arbitrary source treebanks to improve
	target parsing models.},
  url       = {http://aclweb.org/anthology/C16-1002}
}