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

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

16 Jun 2020NAACL (TextGraphs) 2021 6arXiv:2006.09242archive 2025-07-28

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

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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Tasks

Data-to-Text GenerationDecoderKG-to-Text GenerationKnowledge GraphsText Generation

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Data-to-Text Generation WebNLG Graformer BLEU 61.15 #15 of 20 Archive leaderboard report
KG-to-Text Generation AGENDA Graformer BLEU 17.80 #5 of 6 Archive leaderboard report

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerReLUResidual ConnectionSoftmaxTransformer

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