@InProceedings{shi-huang-lee:2017:EMNLP2017,
  author    = {Shi, Tianze  and  Huang, Liang  and  Lee, Lillian},
  title     = {Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {12--23},
  abstract  = {We first present a minimal feature set for transition-based dependency parsing,
	continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross
	and Huang (2016a) of using bi-directional LSTM features. We plug our minimal
	feature set into the dynamic-programming framework of Huang and Sagae (2010)
	and Kuhlmann et al. (2011) to produce the first implementation of worst-case
	O(n\^{}3) exact decoders for arc-hybrid and arc-eager transition systems. With our
	minimal features, we also present O(n\^{}3) global training methods. Finally,
	using ensembles including our new parsers, we achieve the best unlabeled
	attachment score reported (to our knowledge) on the Chinese Treebank and the
	``second-best-in-class'' result on the English Penn Treebank.},
  url       = {https://www.aclweb.org/anthology/D17-1002}
}