@inproceedings{nguyen-verspoor-2018-improved,
    title = "An Improved Neural Network Model for Joint {POS} Tagging and Dependency Parsing",
    author = "Nguyen, Dat Quoc  and
      Verspoor, Karin",
    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/K18-2008/",
    doi = "10.18653/v1/K18-2008",
    pages = "81--91",
    abstract = "We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating a BiLSTM-based tagging component to produce automatically predicted POS tags for the parser. On the benchmark English Penn treebank, our model obtains strong UAS and LAS scores at 94.51{\%} and 92.87{\%}, respectively, producing 1.5+{\%} absolute improvements to the BIST graph-based parser, and also obtaining a state-of-the-art POS tagging accuracy at 97.97{\%}. Furthermore, experimental results on parsing 61 ``big'' Universal Dependencies treebanks from raw texts show that our model outperforms the baseline UDPipe (Straka and Strakova, 2017) with 0.8{\%} higher average POS tagging score and 3.6{\%} higher average LAS score. In addition, with our model, we also obtain state-of-the-art downstream task scores for biomedical event extraction and opinion analysis applications. Our code is available together with all pre-trained models at: \url{https://github.com/datquocnguyen/jPTDP}"
}