@inproceedings{zhang-etal-2019-empirical,
    title = "An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing",
    author = "Zhang, Zhisong  and
      Ma, Xuezhe  and
      Hovy, Eduard",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P19-1562/",
    doi = "10.18653/v1/P19-1562",
    pages = "5592--5598",
    abstract = "In this paper, we investigate the aspect of structured output modeling for the state-of-the-art graph-based neural dependency parser (Dozat and Manning, 2017). With evaluations on 14 treebanks, we empirically show that global output-structured models can generally obtain better performance, especially on the metric of sentence-level Complete Match. However, probably because neural models already learn good global views of the inputs, the improvement brought by structured output modeling is modest."
}