Finally, we obtain the satisfaction distribution y ∈ R |C| as the weighted sum of sentiment distributions of all the customer utterances by: Note that in the training dataset, our approach only needs the dialogue's satisfaction labels while the utterance sentiment labels are unobserved.
we use the categorical cross-entropy loss to minimize the error between the distribution of the output satisfaction polarity and that of the goldstandard satisfaction label of the dialogue by: where g j i is 1 or 0 indicating whether the i th class is a correct answer for the j th training instance, y j i is the predicted satisfaction probability distribution, and Θ denotes the trainable parameter set.