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Amr-to-text Generation With Synchronous Node Replacement Grammar

Linfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, Daniel Gildea . Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) 2017 – 80 citations

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ACL Content Enrichment Interdisciplinary Approaches RAG Training Techniques Variational Autoencoders

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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