@InProceedings{aufrant-wisniewski-yvon:2017:EACLshort,
  author    = {Aufrant, Lauriane  and  Wisniewski, Guillaume  and  Yvon, Fran\c{c}ois},
  title     = {Don't Stop Me Now! Using Global Dynamic Oracles to Correct Training Biases of Transition-Based Dependency Parsers},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {318--323},
  abstract  = {This paper formalizes a sound extension of dynamic oracles to global training,
	in the frame of transition-based dependency parsers. By dispensing with the
	pre-computation of references, this extension widens the training strategies
	that can be entertained for such parsers; we show this by revisiting two
	standard training procedures, early-update and max-violation, to correct some
	of their search space sampling biases. Experimentally, on the SPMRL treebanks,
	this improvement increases the similarity between the train and test
	distributions and yields performance improvements up to 0.7 UAS, without any
	computation overhead.},
  url       = {http://www.aclweb.org/anthology/E17-2051}
}