When the downstream applications only care about the direction of the word vectors (e.g. they only pay attention to the cosine similarity of two words), then normalize, and forget about length.

However, if the downstream applications are able to (or need to) consider more sensible aspects, such as word significance, or consistency in word usage (see below), then normalization might not be such a good idea.