We argue that our joint training procedure can be seen as optimizing the posterior likelihood of the three models.
suppose we place Dirichlet priors on each of the t-tables t st , t sp , t pt as before, but define the prior parameterization using a single hyperparameter α = {α spt } and its marginals such that: Intuitively, α spt represents the number of times a source-pivot-target triplet (s, p, t) was observed.