  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 SemEval-2016 Task 8, our method gives a BLEU score of 25.62, which
is the best reported so far.
