We estimate f (t|s, θ st ) as m i=1 f (x t i |s, θ st ), where x t i is again the i th coordinate of t. Each univariate density f (x t i |s, θ st ) is estimated using a Bayesian linear regression model that predicts the possible representations of the target word from s. However, this is only feasible if s has at most n − 2 coordinates.
we use a low-rank approximation of the source word representations, as follows.