  We present a new dataset of Wikipedia articles each paired with a knowledge
graph, to facilitate the research in conditional text generation, graph
generation and graph representation learning. Existing graph-text paired
datasets typically contain small graphs and short text (1 or few sentences),
thus limiting the capabilities of the models that can be learned on the data.
Our new dataset WikiGraphs is collected by pairing each Wikipedia article from
the established WikiText-103 benchmark (Merity et al., 2016) with a subgraph
from the Freebase knowledge graph (Bollacker et al., 2008). This makes it easy
to benchmark against other state-of-the-art text generative models that are
capable of generating long paragraphs of coherent text. Both the graphs and the
text data are of significantly larger scale compared to prior graph-text paired
datasets. We present baseline graph neural network and transformer model
results on our dataset for 3 tasks: graph -> text generation, graph -> text
retrieval and text -> graph retrieval. We show that better conditioning on the
graph provides gains in generation and retrieval quality but there is still
large room for improvement.
