@inproceedings{ji-etal-2017-fast,
    title = "A Fast and Lightweight System for Multilingual Dependency Parsing",
    author = "Ji, Tao  and
      Wu, Yuanbin  and
      Lan, Man",
    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/Ingest-2025-COMPUTEL/K17-3025/",
    doi = "10.18653/v1/K17-3025",
    pages = "237--242",
    abstract = "We present a multilingual dependency parser with a bidirectional-LSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier. We trained our transition-based projective parser in UD version 2.0 datasets without any additional data. The parser is fast, lightweight and effective on big treebanks. In the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, the official results show that the macro-averaged LAS F1 score of our system Mengest is 61.33{\%}."
}