Therefore, similar to the case at word level, we construct the gold sentence-level attention signals β * for the document d, as illustrated in Figure 2b, and further take the square error as the general loss of the attention at sentence level to supervise the learning process: We employ another Bi-GRU encoder and a softmax output layer to model the ED task, which can handle event triggers with multiple words.
given a sentence s j (j = 1, 2, ..., L) in document d, for each of its word w jt (t = 1, 2, ..., T ), we concatenate its word embedding w jt and entity type embedding e jt with the corresponding document embedding d as the input r jt of the Bi-GRU and thus obtain the hidden state f jt : Finally, we get the probability vector o jt with K dimensions through a softmax layer for w jt , where the k-th element, o jt , of o jt indicates the probability of classifying w jt to the k-th event type.