Abstract
Graph-to-text generation, a subtask of data-to-text generation, aims to generate fluent texts from graph-based data. Many graph-to-text models have shown strong performance in this task employing specialized graph encoders. However, recent approaches employ large pretrained language models (PLMs) achieving state-of-the-art results in data-to-text generation. In this paper, we aim to investigate the impact of large PLMs in graph-to-text generation. We present a study across three graph domains: meaning representations, Wikipedia knowledge graphs (KGs) and scientific KGs. Our analysis shows that PLMs such as BART and T5 achieve state-of-the-art results in graph-to-text benchmarks without explicitly encoding the graph structure. We also demonstrate that task-adaptive pretraining strategies are beneficial to the target task, improving even further the state of the art in two benchmarks for graph-to-text generation. In a final analysis, we investigate possible reasons for the PLMs' success on graph-to-text tasks. We find evidence that their knowledge about the world gives them a big advantage, especially when generating texts from KGs.
Abstract (translated)
URL
https://arxiv.org/abs/2007.08426
