@inproceedings{foland-martin-2017-abstract,
    title = "{A}bstract {M}eaning {R}epresentation Parsing using {LSTM} Recurrent Neural Networks",
    author = "Foland, William  and
      Martin, James H.",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/P17-1043/",
    doi = "10.18653/v1/P17-1043",
    pages = "463--472",
    abstract = "We present a system which parses sentences into Abstract Meaning Representations, improving state-of-the-art results for this task by more than 5{\%}. AMR graphs represent semantic content using linguistic properties such as semantic roles, coreference, negation, and more. The AMR parser does not rely on a syntactic pre-parse, or heavily engineered features, and uses five recurrent neural networks as the key architectural components for inferring AMR graphs."
}