@inproceedings{shi-etal-2017-fast,
    title = "Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set",
    author = "Shi, Tianze  and
      Huang, Liang  and
      Lee, Lillian",
    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://aclanthology.org/D17-1002",
    doi = "10.18653/v1/D17-1002",
    pages = "12--23",
    abstract = "We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our minimal feature set into the dynamic-programming framework of Huang and Sagae (2010) and Kuhlmann et al. (2011) to produce the first implementation of worst-case $O(n^3)$ exact decoders for arc-hybrid and arc-eager transition systems. With our minimal features, we also present $O(n^3)$ global training methods. Finally, using ensembles including our new parsers, we achieve the best unlabeled attachment score reported (to our knowledge) on the Chinese Treebank and the {``}second-best-in-class{''} result on the English Penn Treebank.",
}