@InProceedings{song-EtAl:2018:Long,
  author    = {Song, Linfeng  and  Zhang, Yue  and  Wang, Zhiguo  and  Gildea, Daniel},
  title     = {A Graph-to-Sequence Model for AMR-to-Text Generation},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {1616--1626},
  abstract  = {The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure. Although being able to model non-local semantic information, a sequence LSTM can lose information from the AMR graph structure, and thus facing challenges with large-graphs, which result in long sequences. We introduce a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics. On a standard benchmark, our model shows superior results to existing methods in the literature.},
  url       = {http://www.aclweb.org/anthology/P18-1150}
}