To accomplish this, the model concatenates up to four character output vectors: the {forward, backward} output of the {first, last} character in the token Figure 1a, the four shaded boxes indicate these four outputs for the example token.
the proposed model concatenates all four of these and passes it as input to an multilayer perceptron (MLP): A tag can then be predicted with a linear classifier that takes as input the output of the MLP enized/segmented.