@inproceedings{nguyen-etal-2017-novel,
    title = "A Novel Neural Network Model for Joint {POS} Tagging and Graph-based Dependency Parsing",
    author = "Nguyen, Dat Quoc  and
      Dras, Mark  and
      Johnson, Mark",
    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://preview.aclanthology.org/fix-sig-urls/K17-3014/",
    doi = "10.18653/v1/K17-3014",
    pages = "134--142",
    abstract = "We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to learn feature representations shared for both POS tagging and dependency parsing tasks, thus handling the feature-engineering problem. Our extensive experiments, on 19 languages from the Universal Dependencies project, show that our model outperforms the state-of-the-art neural network-based Stack-propagation model for joint POS tagging and transition-based dependency parsing, resulting in a new state of the art. Our code is open-source and available together with pre-trained models at: \url{https://github.com/datquocnguyen/jPTDP}"
}