To obtain the refined role distribution, we add up M α and I α that we got from the baseline model, and follow that by a softmax layer: To build a representation for sense disambiguation, we simply compute the probability mass for each role (excluding the null role) to obtain r π , and concatenate this with g π and Π • P t : Differently from the role refinement network, sense prediction is predicate-specific.
we first map z π to R dπ , and then take the inner product with the predicate-specific sense embeddings Π to get the refined logits: Similarly to role refinement, σ is the logistic function, W π ∈ R dr×(r−1+dg+dπ) , W π ∈ R m×dr are learned linear mappings.