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arXiv · 2007.08426

Investigating Pretrained Language Models for Graph-to-Text Generation

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

Graph-to-text generation aims to generate fluent texts from graph-based data. In this paper, we investigate two recently proposed pretrained language models (PLMs) and analyze the impact of different task-adaptive pretraining strategies for PLMs in graph-to-text generation. We present a study across three graph domains: meaning representations, Wikipedia knowledge graphs (KGs) and scientific KGs. We show that the PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further. In particular, we report new state-of-the-art BLEU scores of 49.72 on LDC2017T10, 59.70 on WebNLG, and 25.66 on AGENDA datasets - a relative improvement of 31.8%, 4.5%, and 42.4%, respectively. In an extensive analysis, we identify possible reasons for the PLMs' success on graph-to-text tasks. We find evidence that their knowledge about true facts helps them perform well even when the input graph representation is reduced to a simple bag of node and edge labels.

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Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze, Iryna Gurevych. 2020-07-16. Investigating Pretrained Language Models for Graph-to-Text Generation. https://arxiv.org/abs/2007.08426

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