Similar as above, we use one feedforward neural network f coref () to update the hidden word representation h x i for words within each coreference cluster.
the output word vector has the following form: where W coref and b coref are the weights and bias, h C k is a coreference vector calculated by applying max-pooling to all word representations in one cluster: The role of coreference vector is similar to "context vector" utilized in soft attention mechanism (Bahdanau et al., 2015), but we use simple max-pooling instead of computing weights 6 for different word vectors.