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.

该研究论文介绍了一种利用基于类型的问题通过文本预训练在语言模型中灌输细粒度类型知识的方法，并利用知识库文档和知识图谱创建 WikiWiki 数据集来评估性能。该方法在零 - shot 对话状态跟踪基准测试中取得了最先进的表现，可以准确地推断维基百科文章中的实体类型，并能够发现人工评判认为有用的新类型。