  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.

Computation and Language