@inproceedings{le-roux-etal-2019-representation,
    title = "Representation Learning and Dynamic Programming for Arc-Hybrid Parsing",
    author = "Le Roux, Joseph  and
      Rozenknop, Antoine  and
      Lacroix, Mathieu",
    editor = "Bansal, Mohit  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/K19-1023/",
    doi = "10.18653/v1/K19-1023",
    pages = "238--248",
    abstract = "We present a new method for transition-based parsing where a solution is a pair made of a dependency tree and a derivation graph describing the construction of the former. From this representation we are able to derive an efficient parsing algorithm and design a neural network that learns vertex representations and arc scores. Experimentally, although we only train via local classifiers, our approach improves over previous arc-hybrid systems and reach state-of-the-art parsing accuracy."
}