Specifically, the embedding of each word x j is divided into two parts x p j and x r j , and their dimensions are respectively.
the sequence of word vectors An RNN with an nonlinear projection layer is then designed to learn its recurrent state r j for each word over where W r ∈ R dr×dr is a parameter matrix and b r ∈ R dr is a bias item.