@inproceedings{kurtz-etal-2019-improving,
    title = "Improving Semantic Dependency Parsing with Syntactic Features",
    author = "Kurtz, Robin  and
      Roxbo, Daniel  and
      Kuhlmann, Marco",
    editor = {Nivre, Joakim  and
      Derczynski, Leon  and
      Ginter, Filip  and
      Lindi, Bj{\o}rn  and
      Oepen, Stephan  and
      S{\o}gaard, Anders  and
      Tidemann, J{\"o}rg},
    booktitle = "Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://aclanthology.org/W19-6202",
    pages = "12--21",
    abstract = "We extend a state-of-the-art deep neural architecture for semantic dependency parsing with features defined over syntactic dependency trees. Our empirical results show that only gold-standard syntactic information leads to consistent improvements in semantic parsing accuracy, and that the magnitude of these improvements varies with the specific combination of the syntactic and the semantic representation used. In contrast, automatically predicted syntax does not seem to help semantic parsing. Our error analysis suggests that there is a significant overlap between syntactic and semantic representations.",
}