The design choices for the BiLSTM in this step remain the same as in the baseline model.
then, we transform the feature vector v extracted from the text X to a score vectorŷ (a) for each aspect a ∈ A and apply softmax normalization:ŷ (a)  where for each aspect, we predict its presence or absence as well as its polarity in one step: the loss is simply the cross entropy summed over all aspects: with   Keeping the formalization as an end-to-end task, we replace the BiLStM by a convolutional neural network (CNN) as described in (Kim, 2014).