A Synchronous Hyperedge Replacement Grammar based approach for AMR parsing
Xiaochang Peng, Linfeng Song, Daniel Gildea · 2015
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 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.