@inproceedings{ji-etal-2018-antnlp,
    title = "{A}nt{NLP} at {C}o{NLL} 2018 Shared Task: A Graph-Based Parser for {U}niversal {D}ependency Parsing",
    author = "Ji, Tao  and
      Liu, Yufang  and
      Wang, Yijun  and
      Wu, Yuanbin  and
      Lan, Man",
    editor = "Zeman, Daniel  and
      Haji{\v{c}}, Jan",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/K18-2025/",
    doi = "10.18653/v1/K18-2025",
    pages = "248--255",
    abstract = "We describe the graph-based dependency parser in our system (AntNLP) submitted to the CoNLL 2018 UD Shared Task. We use bidirectional lstm to get the word representation, then a bi-affine pointer networks to compute scores of candidate dependency edges and the MST algorithm to get the final dependency tree. From the official testing results, our system gets 70.90 LAS F1 score (rank 9/26), 55.92 MLAS (10/26) and 60.91 BLEX (8/26)."
}