@inproceedings{ballesteros-carreras-2017-arc,
    title = "Arc-Standard Spinal Parsing with Stack-{LSTM}s",
    author = "Ballesteros, Miguel  and
      Carreras, Xavier",
    editor = "Miyao, Yusuke  and
      Sagae, Kenji",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W17-6316/",
    pages = "115--121",
    abstract = "We present a neural transition-based parser for spinal trees, a dependency representation of constituent trees. The parser uses Stack-LSTMs that compose constituent nodes with dependency-based derivations. In experiments, we show that this model adapts to different styles of dependency relations, but this choice has little effect for predicting constituent structure, suggesting that LSTMs induce useful states by themselves."
}