Unfortunately, both output normalization and gradient computation require computation of the score for every word in V, which is the bottleneck during training, since it implies product of very large matrices (|V| being usually anywhere from tens to hundreds of thousand words).
the use of the uniform distribution yields more consistent results, despite the fact that it is slow to learn.