Many NLP applications can be framed as a graph-to-sequence learning problem. Previous work proposing neural architectures on this setting obtained promising results compared to grammar-based approaches but still rely on linearisation heuristics and or standard recurrent networks to achieve the best performance. In this work, we propose a new model that encodes the full structural information contained in the graph. Our architecture couples the recently proposed Gated Graph Neural Networks with an input transformation that allows nodes and edges to have their own hidden representations, while tackling the parameter explosion problem present in previous work. Experimental results show that our model outperforms strong baselines in generation from AMR graphs and syntax-based neural machine translation.
Early NLP approaches for this problem were based on Hyperedge Replacement Grammars [][HRGs] Drewes1997 . These grammars assume the transduction problem can be split into rules that map portions of a graph to a set of tokens in the output sequence. In particular, defines a parsing algorithm, followed by a complexity analysis, while report experiments on semantic-based machine translation using HRGs. HRGs were also used in previous work on AMR parsing @cite_5 . The main drawback of these grammar-based approaches though is the need for alignments between graph nodes and surface tokens, which are usually not available in gold-standard form.
@cite_5
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This paper presents a synchronous-graphgrammar-based approach for string-toAMR 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-toAMR 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.