pv ← ∝ depth in tree 7: Bv ∼ Ber(pv) 8: if Bv then 9: ln ← ln + L(ŷv, y) 10: ifŷv > τ then 11: e ← 0 15: while e < epochs do 16: for each instance xi ∈ x do 17: hi ∆θ ← BACKPROP(li) 21: θ ← θ + α∆θ 22: e ← e + 1 return θ Incurring loss for all nodes along the path specified by y would place a disproportionate amount of emphasis on correctly applying terms that are 'higher' in the ontology, as loss will be propagated for the initial predictions concerning the application of these and then also, due to recursive application, for all of their children (and so on).
we only incur (and hence backpropagate) loss for a node v stochastically, according to a Bernoulli distribution B with parameter p v .