@inproceedings{aufrant-etal-2018-exploiting,
    title = "Exploiting Dynamic Oracles to Train Projective Dependency Parsers on Non-Projective Trees",
    author = "Aufrant, Lauriane  and
      Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/N18-2066/",
    doi = "10.18653/v1/N18-2066",
    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."
}