@inproceedings{ballesteros-al-onaizan-2017-amr,
    title = "{AMR} Parsing using Stack-{LSTM}s",
    author = "Ballesteros, Miguel  and
      Al-Onaizan, Yaser",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/D17-1130/",
    doi = "10.18653/v1/D17-1130",
    pages = "1269--1275",
    abstract = "We present a transition-based AMR parser that directly generates AMR parses from plain text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our experiments, we show that our parser achieves very competitive scores on English using only AMR training data. Adding additional information, such as POS tags and dependency trees, improves the results further."
}