AMR-to-text Generation with Synchronous Node Replacement Grammar

Abstract

This paper addresses the task of AMR-totext 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.