We present a novel encoder-decoder architecture for graph-to-text generation based on Transformer, called the Graformer . With our novel graph self-attention, every node in the input graph is taken into account for the encoding of every other node - not only direct neighbors , facilitating the detection of global patterns. For this, the relation between any two nodes is characterized by the length of the shortest path between them , including the special case when there is no such path. The Graformer learns to weigh these node-node relations differently for different attention heads, thus virtually learning differently connected views of the input graph. We evaluate the Graformer on two  graph-to-text generation benchmarks, the AGENDA dataset and the WebNLGchallenge dataset , where it achieves strong performance while using significantly less parameters than other approaches.
We present Graformer, a novel Transformer-based encoder-decoder architecture for graph-to-text generation . With our novel graph self-attention, the encoding of a node relies on all nodes in the input graph - not only direct neighbors - facilitating the detection of global patterns. We represent the relation between two nodes as the length of the shortest path between them . Graformer learns to weight these node-node relations differently for different attention heads, thus virtually learning differently connected views of the input graph. We evaluate Graformer on two popular graph-to-text generation benchmarks, AGENDA and WebNLG , where it achieves strong performance while using many fewer parameters than other approaches.
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