These widely-used representations often significantly improve performance in downstream tasks, as they are able to leverage large amounts of unstructured data.
in doing so, we embrace the suggestion of Gladkova and Drozd (2016), which argue for "a shift from absolute ratings of word embeddings towards more exploratory evaluations that would aim not for generic scores, but for identification of strengths and weaknesses of embeddings", and conduct rigorous analysis of each representation.