(2015) found so effective in a different structured prediction architecture.
the k th -level BiLStM ( Figure 6) reads a sequence of input vectors x k) , and produces a sequence of vectors γ After this deep generalization, we define γ i:j to be the concatenation of all γ this novel deep BiLStM architecture has more connections than a pair of deep LStMs, since α while we may informally regard α (k) i as being a deep summary of the prefix x 0:i , it actually depends indirectly on all of x (except when k = 1).