Given the authentic bilingual corpus D b , Bayesian neural networks aim at finding the posterior distribution over model parameters P (θ y→x |D b ).
with a target sentence y in the monolingual corpus D m and its translationx, the translation probability is given by we are interested in calculating the variance of the distribution P (x|y, θ y→x ) that reflects our ignorance over model parameters, which is referred to as model uncertainty.