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A Synchronous Hyperedge Replacement Grammar based approach for AMR parsing
2015
Proceedings of the Nineteenth Conference on Computational Natural Language Learning
This paper presents a synchronous-graphgrammar-based approach for string-to-AMR parsing. We apply Markov Chain Monte Carlo (MCMC) algorithms to learn Synchronous Hyperedge Replacement Grammar (SHRG) rules from a forest that represents likely derivations consistent with a fixed string-to-graph alignment. We make an analogy of string-to-AMR parsing to the task of phrase-based machine translation and come up with an efficient algorithm to learn graph grammars from string-graph pairs. We propose an
doi:10.18653/v1/k15-1004
dblp:conf/conll/PengSG15
fatcat:cal7cudlardmfg275r6mrwgefi