A Synchronous Hyperedge Replacement Grammar based approach for AMR parsing

Xiaochang Peng, Linfeng Song, Daniel Gildea
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
more » ... effective approximation strategy to resolve the complexity issue of graph compositions. We also show some useful strategies to overcome existing problems in an SHRG-based parser and present preliminary results of a graph-grammar-based approach.
doi:10.18653/v1/k15-1004 dblp:conf/conll/PengSG15 fatcat:cal7cudlardmfg275r6mrwgefi