Before revision:  We also demonstrate that task-adaptive pretraining strategies are beneficial to the target task, improving even furtherthe 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.
After revision:  We also demonstrate 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 investigate possible reasons for the PLMs' success on graph-to-text tasks.