The decoder is to generate a response Y given the hidden representations of the input utterance X under some specificity level denoted by the control variable s. Specifically, at step t, we define the probability of generating any target word y t by a "mixture" of probabilities: where p M (y t ) denotes the semantic-based generation probability, p S (y t ) denotes the specificitybased generation probability, β and γ are the coefficients.
p M (y t ) is defined the same as that in traditional Seq2Seq model (Sutskever et al., 2014): where w is a one-hot indicator vector of the word w and e t−1 is the semantic representation of the t − 1-th generated word in decoder.