@inproceedings{chen-etal-2018-simple,
    title = "A Simple yet Effective Joint Training Method for Cross-Lingual {U}niversal {D}ependency Parsing",
    author = "Chen, Danlu  and
      Lin, Mengxiao  and
      Hu, Zhifeng  and
      Qiu, Xipeng",
    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/K18-2026/",
    doi = "10.18653/v1/K18-2026",
    pages = "256--263",
    abstract = "This paper describes Fudan{'}s submission to CoNLL 2018{'}s shared task Universal Dependency Parsing. We jointly train models when two languages are similar according to linguistic typology and then ensemble the models using a simple re-parse algorithm. We outperform the baseline method by 4.4{\%} (2.1{\%}) on average on development (test) set in CoNLL 2018 UD Shared Task."
}