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arXiv · 2210.10599

Self-supervised Graph Masking Pre-training for Graph-to-Text Generation

Jiuzhou Han·Ehsan Shareghi
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Abstract

Large-scale pre-trained language models (PLMs) have advanced Graph-to-Text (G2T) generation by processing the linearised version of a graph. However, the linearisation is known to ignore the structural information. Additionally, PLMs are typically pre-trained on free text which introduces domain mismatch between pre-training and downstream G2T generation tasks. To address these shortcomings, we propose graph masking pre-training strategies that neither require supervision signals nor adjust the architecture of the underlying pre-trained encoder-decoder model. When used with a pre-trained T5, our approach achieves new state-of-the-art results on WebNLG+2020 and EventNarrative G2T generation datasets. Our method also shows to be very effective in the low-resource setting.

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Jiuzhou Han, Ehsan Shareghi. 2022-10-19. Self-supervised Graph Masking Pre-training for Graph-to-Text Generation. https://arxiv.org/abs/2210.10599

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