@InProceedings{wang-xue:2017:EMNLP2017,
  author    = {Wang, Chuan  and  Xue, Nianwen},
  title     = {Getting the Most out of AMR Parsing},
  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     = {1257--1268},
  abstract  = {This paper proposes to tackle the AMR parsing bottleneck by improving two
	components of an AMR parser: concept identification and alignment. We first
	build a Bidirectional LSTM based concept identifier that is able to incorporate
	richer contextual information to learn sparse AMR concept labels. We then
	extend an HMM-based word-to-concept alignment model with graph distance
	distortion and a rescoring method during decoding to incorporate the structural
	information in the AMR graph. We show integrating the two components into an
	existing AMR parser results in consistently better performance over the state
	of the art on various datasets.},
  url       = {https://www.aclweb.org/anthology/D17-1129}
}