  Understanding human language often necessitates understanding entities and
their place in a taxonomy of knowledge -- their types. Previous methods to
learn entity types rely on training classifiers on datasets with coarse, noisy,
and incomplete labels. We introduce a method to instill fine-grained type
knowledge in language models with text-to-text pre-training on type-centric
questions leveraging knowledge base documents and knowledge graphs. We create
the WikiWiki dataset: entities and passages from 10M Wikipedia articles linked
to the Wikidata knowledge graph with 41K types. Models trained on WikiWiki
achieve state-of-the-art performance in zero-shot dialog state tracking
benchmarks, accurately infer entity types in Wikipedia articles, and can
discover new types deemed useful by human judges.
