@inproceedings{dozat-etal-2017-stanfords,
    title = "{S}tanford{'}s Graph-based Neural Dependency Parser at the {C}o{NLL} 2017 Shared Task",
    author = "Dozat, Timothy  and
      Qi, Peng  and
      Manning, Christopher D.",
    editor = "Haji{\v{c}}, Jan  and
      Zeman, Dan",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/K17-3002",
    doi = "10.18653/v1/K17-3002",
    pages = "20--30",
    abstract = "This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the rare word problem that abounds in languages with complex morphology, we include a character-based word representation that uses an LSTM to produce embeddings from sequences of characters. Our system was ranked first according to all five relevant metrics for the system: UPOS tagging (93.09{\%}), XPOS tagging (82.27{\%}), unlabeled attachment score (81.30{\%}), labeled attachment score (76.30{\%}), and content word labeled attachment score (72.57{\%}).",
}