These widely-used representations often significantly improve performance in downstream tasks, as they are able to leverage large amounts of unstructured data.
most of the popular collections of word embeddings assign only one vector to each word, thus shifting the burden of word disambiguation to deeper, task-specific layers, which commonly rely on data of much smaller scales.