  Prior work on Data-To-Text Generation, the task of converting knowledge graph
(KG) triples into natural text, focused on domain-specific benchmark datasets.
In this paper, however, we verbalize the entire English Wikidata KG, and
discuss the unique challenges associated with a broad, open-domain, large-scale
verbalization. We further show that verbalizing a comprehensive, encyclopedic
KG like Wikidata can be used to integrate structured KGs and natural language
corpora. In contrast to the many architectures that have been developed to
integrate these two sources, our approach converts the KG into natural text,
allowing it to be seamlessly integrated into existing language models. It
carries the further advantages of improved factual accuracy and reduced
toxicity in the resulting language model. We evaluate this approach by
augmenting the retrieval corpus in a retrieval language model and showing
significant improvements on the knowledge intensive tasks of open domain QA and
the LAMA knowledge probe.
