Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communication between two distant nodes, thereby neglecting graph topology as all nodes are  connected. In contrast, local node encoding considers the relations between directly connected nodes capturing the graph structure, but it can fail to capture long-range relations. In this work, we gather the best of both encoding strategies, proposing novel models that encode an input graph combining both global and local node contexts . Our approaches are able to learn better contextualized node embeddings for text generation . In our experiments, we demonstrate that our models lead to significant improvements in KG-to-text generation, achieving BLEU scores of 17.81 on AGENDA dataset, and 63.10 on the WebNLG dataset for seen categories, outperforming the state of the art by 3.51 and 2.51 points, respectively.
Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communication between two distant nodes, thereby neglecting graph topology as all nodes are directly connected. In contrast, local node encoding considers the relations between neighbor nodes capturing the graph structure, but it can fail to capture long-range relations. In this work, we gather both encoding strategies, proposing novel neural models which encode an input graph combining both global and local node contexts , in order to learn better contextualized node embeddings . In our experiments, we demonstrate that our approaches lead to significant improvements on two graph-to-text datasets achieving BLEU scores of 18.01 on AGENDA dataset, and 63.69 on the WebNLG dataset for seen categories, outperforming state-of-the-art models by 3.7 and 3.1 points, respectively.
A
directly
R
directly connected
neighbor
D
the best of
R
models that
neural models which
R
. Our approaches are able
, in order
D
for text generation
R
models
approaches
R
in KG-to-text generation,
on two graph-to-text datasets
R
17.81
18.01
R
63.10
63.69
R
the state of the art by 3.51 and 2.51
state-of-the-art models by 3.7 and 3.1