Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs

Martin Schmitt, Leonardo F. R. Ribeiro, Philipp Dufter, Iryna Gurevych, Hinrich Schuetze

Abstract:

We present Graformer, a novel Transformerbased encoder-decoder architecture for graphto-text generation. With our novel graph selfattention, 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 nodenode 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.