Then, we turn on coverage training, as advised by See et al.
we let the RNN generate the special vectors at time step t (i) by linearly embedding the RNN input x t ∈ R N x to an embedded inputε t ∈ R N h , and (ii) by obtaining a target memory τ t as a linear combination of the current input x t (projected in the hidden space) and the previous history h t−1 (after a linear transformation).