A contextualized language embedding is a numerical representation of a word or phrase that changes according to its surrounding context. Unlike static embeddings, it can represent different meanings of the same word depending on how the word is used.
An embedding that comes close to "understanding" words and phrases in ways that fluent human speakers can. Contextualized language embeddings can understand complex syntax, semantics, and context. For example, consider embeddings of the English word cow . Older embeddings such as word2vec can represent English words such that the distance in the embedding space from cow to bull is similar to the distance from ewe (female sheep) to ram (male sheep) or from female to male . Contextualized language embeddings can go a step further by recognizing that English speakers sometimes casually use the word cow to mean either cow or bull.