Graph-to-Sequence Learning using Gated Graph Neural Networks

ACL 2018 Daniel BeckGholamreza HaffariTrevor Cohn

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... (read more)

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