Abstract
We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with graphs, we investigate the problem of encoding graphs using graph convolutional networks (GCNs). Unlike various existing approaches where shallow architectures were used for capturing local structural information only, we introduce a dense connection strategy, proposing a novel Densely Connected Graph Convolutional Network (DCGCN). Such a deep architecture is able to integrate both local and non-local features to learn a better structural representation of a graph. Our model outperforms the state-of-the-art neural models significantly on AMR-to-text generation and syntax-based neural machine translation.
Highlights
Graphs play an important role in natural language processing (NLP) as they are able to capture richer structural information than sequences and trees
We compare the performance of Densely Connected Graph Convolutional Network (DCGCN) with the other three kinds of models: (1) sequence-tosequence (Seq2Seq) models, which use linearized graphs as inputs; (2) recurrent graph encoders (GGNN2Seq, GraphLSTM); (3) models trained with external resources
Our single DCGCN model consistently outperforms Seq2Seq models by a significant margin when trained without external resources
Summary
Graphs play an important role in natural language processing (NLP) as they are able to capture richer structural information than sequences and trees. The abstract meaning representation (AMR) (Banarescu et al, 2013) is a directed, labeled graph as shown, where nodes in the graph denote semantic concepts and edges denote relations between concepts. Such graph representations can capture rich semanticlevel structural information, and are attractive representations useful for semantics-related tasks such as semantic parsing (Guo and Lu, 2018) and natural language generation (Beck et al, 2018). L layers will be needed in order to capture neighborhood information that is L hops away
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