@inproceedings{onder-etal-2018-sparse,
    title = "{SP}arse: {K}o{\c{c}} {U}niversity Graph-Based Parsing System for the {C}o{NLL} 2018 Shared Task",
    author = {{\"O}nder, Berkay  and
      G{\"u}meli, Can  and
      Yuret, Deniz},
    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/fix-sig-urls/K18-2022/",
    doi = "10.18653/v1/K18-2022",
    pages = "216--222",
    abstract = "We present SParse, our Graph-Based Parsing model submitted for the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (Zeman et al., 2018). Our model extends the state-of-the-art biaffine parser (Dozat and Manning, 2016) with a structural meta-learning module, SMeta, that combines local and global label predictions. Our parser has been trained and run on Universal Dependencies datasets (Nivre et al., 2016, 2018) and has 87.48{\%} LAS, 78.63{\%} MLAS, 78.69{\%} BLEX and 81.76{\%} CLAS (Nivre and Fang, 2017) score on the Italian-ISDT dataset and has 72.78{\%} LAS, 59.10{\%} MLAS, 61.38{\%} BLEX and 61.72{\%} CLAS score on the Japanese-GSD dataset in our official submission. All other corpora are evaluated after the submission deadline, for whom we present our unofficial test results."
}