@inproceedings{wisniewski-etal-2018-automatically,
    title = "Automatically Selecting the Best Dependency Annotation Design with Dynamic Oracles",
    author = "Wisniewski, Guillaume  and
      Lacroix, Oph{\'e}lie  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/N18-2064/",
    doi = "10.18653/v1/N18-2064",
    pages = "401--406",
    abstract = "This work introduces a new strategy to compare the numerous conventions that have been proposed over the years for expressing dependency structures and discover the one for which a parser will achieve the highest parsing performance. Instead of associating each sentence in the training set with a single gold reference we propose to consider a set of references encoding alternative syntactic representations. Training a parser with a dynamic oracle will then automatically select among all alternatives the reference that will be predicted with the highest accuracy. Experiments on the UD corpora show the validity of this approach."
}