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

• URL: http://arxiv.org/abs/2006.09242v3
• Date: Tue, 27 Apr 2021 09:13:08 GMT
• Title: Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs
• Authors: Martin Schmitt, Leonardo F. R. Ribeiro, Philipp Dufter, Iryna Gurevych, Hinrich Sch\"utze
• Abstract summary: 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. Graformer learns to weight these node-node relations differently for different attention heads, thus virtually learning differently connected views of the input graph.
• Score: 54.176285420428776
• License: http://creativecommons.org/licenses/by/4.0/
• 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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