  Pretrained Language Models (PLMs) benefit from external knowledge stored in
graph structures for various downstream tasks. However, bridging the modality
gap between graph structures and text remains a significant challenge.
Traditional methods like linearizing graphs for PLMs lose vital graph
connectivity, whereas Graph Neural Networks (GNNs) require cumbersome processes
for integration into PLMs. In this work, we propose a novel graph-guided
self-attention mechanism, GraSAME. GraSAME seamlessly incorporates token-level
structural information into PLMs without necessitating additional alignment or
concatenation efforts. As an end-to-end, lightweight multimodal module, GraSAME
follows a multi-task learning strategy and effectively bridges the gap between
graph and textual modalities, facilitating dynamic interactions between GNNs
and PLMs. Our experiments on the graph-to-text generation task demonstrate that
GraSAME outperforms baseline models and achieves results comparable to
state-of-the-art (SOTA) models on WebNLG datasets. Furthermore, compared to
SOTA models, GraSAME eliminates the need for extra pre-training tasks to adjust
graph inputs and reduces the number of trainable parameters by over 100
million.
