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arXiv · 2006.09242

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

Martin Schmitt·Leonardo F. R. Ribeiro·Philipp Dufter·Iryna Gurevych·Hinrich Schütze
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Abstract

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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Martin Schmitt, Leonardo F. R. Ribeiro, Philipp Dufter, Iryna Gurevych, Hinrich Schütze. 2021-04-27. Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs. https://arxiv.org/abs/2006.09242

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