WORD2GAUSS (Vilnis and McCallum, 2015) represents words as multivariate K-dimensional Gaussians rather than points in the embedding space: it is therefore naturally asymmetric and was used in LE tasks before, but its performance on Hy-perLex indicates that it cannot effectively capture the subtleties required to model graded LE.
note that the comparison is not strictly fair as WORD2GAUSS does not leverage any external knowledge.