@InProceedings{ballesteros-alonaizan:2017:EMNLP2017,
  author    = {Ballesteros, Miguel  and  Al-Onaizan, Yaser},
  title     = {AMR Parsing using Stack-LSTMs},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {https://www.aclweb.org/anthology/D17-1130}
}