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Large-scale pre-trained language models (PLMs) have advanced Graph-to-Text(G2T) generation by processing the linearised version of a graph. However, thelinearisation is known to ignore the structural information. Additionally, PLMsare typically pre-trained on free text which introduces domain mismatch betweenpre-training and downstream G2T generation tasks. To address theseshortcomings, we propose graph masking pre-training strategies that neitherrequire supervision signals nor adjust the architecture of the underlyingpre-trained encoder-decoder model. When used with a pre-trained T5, ourapproach achieves new state-of-the-art results on WebNLG+2020 andEventNarrative G2T generation datasets. Our method also shows to be veryeffective in the low-resource setting.

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