  Contextual word representations, typically trained on unstructured, unlabeled
text, do not contain any explicit grounding to real world entities and are
often unable to remember facts about those entities. We propose a general
method to embed multiple knowledge bases (KBs) into large scale models, and
thereby enhance their representations with structured, human-curated knowledge.
For each KB, we first use an integrated entity linker to retrieve relevant
entity embeddings, then update contextual word representations via a form of
word-to-entity attention. In contrast to previous approaches, the entity
linkers and self-supervised language modeling objective are jointly trained
end-to-end in a multitask setting that combines a small amount of entity
linking supervision with a large amount of raw text. After integrating WordNet
and a subset of Wikipedia into BERT, the knowledge enhanced BERT (KnowBert)
demonstrates improved perplexity, ability to recall facts as measured in a
probing task and downstream performance on relationship extraction, entity
typing, and word sense disambiguation. KnowBert's runtime is comparable to
BERT's and it scales to large KBs.
