Our proposal is to capture both types of clues by incorporating a max pooling layer on top of the base model.
as illustrated in Figure 3a, for each word w t , we integrate the intermediate representation of label prediction for each predicate h K i,t by applying max pooling to the vectors that represent pairs of prediction information for two predicates h K i,t and h K j,t (including the case i = j): In this equation, maxpool is an operation to extract the maximum value of each dimension in The newly obtained vector h i,t for w p i and w t is input into the softmax layer in the same manner as in the base model.