@inproceedings{li-etal-2018-joint-learning,
    title = "Joint Learning of {POS} and Dependencies for Multilingual {U}niversal {D}ependency Parsing",
    author = "Li, Zuchao  and
      He, Shexia  and
      Zhang, Zhuosheng  and
      Zhao, Hai",
    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/add-emnlp-2024-awards/K18-2006/",
    doi = "10.18653/v1/K18-2006",
    pages = "65--73",
    abstract = "This paper describes the system of team LeisureX in the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system predicts the part-of-speech tag and dependency tree jointly. For the basic tasks, including tokenization, lemmatization and morphology prediction, we employ the official baseline model (UDPipe). To train the low-resource languages, we adopt a sampling method based on other richresource languages. Our system achieves a macro-average of 68.31{\%} LAS F1 score, with an improvement of 2.51{\%} compared with the UDPipe."
}