The former two works place nonparametric priors (also known as cache models) on the parameters and utilize Gibbs sampling.
for each source word type e, we assume the prior distribution for t e = t e,1 • • • t e,V f , which is itself a distribution over the target vocabulary, to be a Dirichlet distribution (with its own set of hyperparameters Θ e = θ e,1 • • • θ e,V f ) independent from the priors of other source word types: t e ∼ Dirichlet(t e ; Θ e ) f j |a, e, T ∼ Multinomial(f j ; t ea j ) We choose symmetric Dirichlet priors identically for all source words e with θ e,f = θ = 0.0001 to obtain a sparse Dirichlet prior.