  We introduce EventNarrative, a knowledge graph-to-text dataset from publicly
available open-world knowledge graphs. Given the recent advances in
event-driven Information Extraction (IE), and that prior research on
graph-to-text only focused on entity-driven KGs, this paper focuses on
event-centric data. However, our data generation system can still be adapted to
other other types of KG data. Existing large-scale datasets in the
graph-to-text area are non-parallel, meaning there is a large disconnect
between the KGs and text. The datasets that have a paired KG and text, are
small scale and manually generated or generated without a rich ontology, making
the corresponding graphs sparse. Furthermore, these datasets contain many
unlinked entities between their KG and text pairs. EventNarrative consists of
approximately 230,000 graphs and their corresponding natural language text, 6
times larger than the current largest parallel dataset. It makes use of a rich
ontology, all of the KGs entities are linked to the text, and our manual
annotations confirm a high data quality. Our aim is two-fold: help break new
ground in event-centric research where data is lacking, and to give researchers
a well-defined, large-scale dataset in order to better evaluate existing and
future knowledge graph-to-text models. We also evaluate two types of baseline
on EventNarrative: a graph-to-text specific model and two state-of-the-art
language models, which previous work has shown to be adaptable to the knowledge
graph-to-text domain.

Computation and Language