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arXiv · 1702.00500

AMR-to-text Generation with Synchronous Node Replacement Grammar

Linfeng Song·Xiaochang Peng·Yue Zhang·Zhiguo Wang·Daniel Gildea
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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.

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Linfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, Daniel Gildea. 2017-02-01. AMR-to-text Generation with Synchronous Node Replacement Grammar. https://arxiv.org/abs/1702.00500

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