@inproceedings{wang-etal-2021-enhanced,
    title = "Enhanced {U}niversal {D}ependency Parsing with Automated Concatenation of Embeddings",
    author = "Wang, Xinyu  and
      Jia, Zixia  and
      Jiang, Yong  and
      Tu, Kewei",
    editor = "Oepen, Stephan  and
      Sagae, Kenji  and
      Tsarfaty, Reut  and
      Bouma, Gosse  and
      Seddah, Djam{\'e}  and
      Zeman, Daniel",
    booktitle = "Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.iwpt-1.20/",
    doi = "10.18653/v1/2021.iwpt-1.20",
    pages = "189--195",
    abstract = "This paper describe the system used in our submission to the \textit{IWPT 2021 Shared Task}. Our system is a graph-based parser with the technique of Automated Concatenation of Embeddings (ACE). Because recent work found that better word representations can be obtained by concatenating different types of embeddings, we use ACE to automatically find the better concatenation of embeddings for the task of enhanced universal dependencies. According to official results averaged on 17 languages, our system rank 2nd over 9 teams."
}