@inproceedings{corro-etal-2017-efficient,
    title = "Efficient Discontinuous Phrase-Structure Parsing via the Generalized Maximum Spanning Arborescence",
    author = "Corro, Caio  and
      Le Roux, Joseph  and
      Lacroix, Mathieu",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/D17-1172/",
    doi = "10.18653/v1/D17-1172",
    pages = "1644--1654",
    abstract = "We present a new method for the joint task of tagging and non-projective dependency parsing. We demonstrate its usefulness with an application to discontinuous phrase-structure parsing where decoding lexicalized spines and syntactic derivations is performed jointly. The main contributions of this paper are (1) a reduction from joint tagging and non-projective dependency parsing to the Generalized Maximum Spanning Arborescence problem, and (2) a novel decoding algorithm for this problem through Lagrangian relaxation. We evaluate this model and obtain state-of-the-art results despite strong independence assumptions."
}