  Graph-to-text generation has benefited from pre-trained language models
(PLMs) in achieving better performance than structured graph encoders. However,
they fail to fully utilize the structure information of the input graph. In
this paper, we aim to further improve the performance of the pre-trained
language model by proposing a structured graph-to-text model with a two-step
fine-tuning mechanism which first fine-tunes the model on Wikipedia before
adapting to the graph-to-text generation. In addition to using the traditional
token and position embeddings to encode the knowledge graph (KG), we propose a
novel tree-level embedding method to capture the inter-dependency structures of
the input graph. This new approach has significantly improved the performance
of all text generation metrics for the English WebNLG 2017 dataset.
