cascading effects ∵) meanings of identiﬁers depend on that of others disagreements between Annotator 1 & 2 are mostly due to a single disagreement for D some declarations are not clear enough 113 disagreements between Annotator 1 & 3 can be categorized into 40 patterns we are given a training set D of N training points (n, tn), with n = 1, . . . , N, where the variables n are the inputs [Simeone, 2018] Under this assumption, the data set D is not necessary, since the mapping between input and output is fully described by the distribution p(, t). [Simeone, 2018] 13 / 17