AMR-to-text Generation with Synchronous Node Replacement Grammar

ACL 2017

AMR-to-text Generation with Synchronous Node Replacement Grammar

Jan 27, 2021
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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 SemEval-2016 Task 8, our method gives a BLEU score of 25.62, which is the best reported so far. Authors: Linfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, Daniel Gildea (University of Rochester, Singapore University of Technology and Design, IBM)

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