What does "contextual embeddings" represent?

Contextual embeddings refer to word representations that are influenced by the surrounding context in which a word appears. Unlike static word representations, which assign a single fixed vector to a word regardless of its usage, contextual embeddings provide a more nuanced understanding by generating different vectors for the same word in different contexts. This is crucial because many words have multiple meanings, and their interpretation can significantly change based on the words around them. For instance, the word "bank" can refer to a financial institution or the side of a river, and contextual embeddings allow models to capture these distinctions accurately.

This dynamic representation is achieved through advanced techniques like transformers, which utilize the context of surrounding words to derive a more accurate understanding of word meanings as they relate to their specific usages within sentences. Contextual embeddings thus offer a powerful tool for natural language processing tasks, as they enable models to better grasp the intricacies of human language.