@InProceedings{aufrant-wisniewski-yvon:2018:N18-2,
  author    = {Aufrant, Lauriane  and  Wisniewski, Guillaume  and  Yvon, François},
  title     = {Exploiting Dynamic Oracles to Train Projective Dependency Parsers on Non-Projective Trees},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {413--419},
  abstract  = {Because the most common transition systems are projective, training a transition-based dependency parser often implies to either ignore or rewrite the non-projective training examples, which has an adverse impact on accuracy. In this work, we propose a simple modification of dynamic oracles, which enables the use of non-projective data when training projective parsers. Evaluation on 73 treebanks shows that our method achieves significant gains (+2 to +7 UAS for the most non-projective languages) and consistently outperforms traditional projectivization and pseudo-projectivization approaches.},
  url       = {http://www.aclweb.org/anthology/N18-2066}
}