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.
Besides graph-based approaches, there exist also other strategies on AMR parsing: REF learn synchronous hyperedge replacement grammar rules from string- graph pairs.
A Synchronous Hyperedge Replacement Grammar based approach for AMR parsing
CoNLL
Computer Science