@inproceedings{song-etal-2017-amr,
    title = "{AMR}-to-text Generation with Synchronous Node Replacement Grammar",
    author = "Song, Linfeng  and
      Peng, Xiaochang  and
      Zhang, Yue  and
      Wang, Zhiguo  and
      Gildea, Daniel",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P17-2002",
    doi = "10.18653/v1/P17-2002",
    pages = "7--13",
    abstract = "This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar. During training, graph-to-string rules are learned using a heuristic extraction algorithm. At test time, a graph transducer is applied to collapse input AMRs and generate output sentences. Evaluated on a standard benchmark, our method gives the state-of-the-art result.",
}