@inproceedings{dozat-manning-2018-simpler,
    title = "Simpler but More Accurate Semantic Dependency Parsing",
    author = "Dozat, Timothy  and
      Manning, Christopher D.",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/P18-2077/",
    doi = "10.18653/v1/P18-2077",
    pages = "484--490",
    abstract = "While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture between-word relationships that are more closely related to the meaning of a sentence, using graph-structured representations. We extend the LSTM-based syntactic parser of Dozat and Manning (2017) to train on and generate these graph structures. The resulting system on its own achieves state-of-the-art performance, beating the previous, substantially more complex state-of-the-art system by 0.6{\%} labeled F1. Adding linguistically richer input representations pushes the margin even higher, allowing us to beat it by 1.9{\%} labeled F1."
}