We first explain the utterance encoder module, which uses a neural network f ACT to encode the dialog history (i.e., concatenation of previous utterances from both the user and the system turns where The overall featureū is used to predict the hierarchical representation of dialog act.
we output a vector P θ (A) ∈ R H 0 , whose i th component gives the probability of the i th node in the dialog act graph being activated: where V a ∈ R D×H 0 is the attention matrix, the weights W u , W b , b are the learnable parameters to project the input to R D space, and σ is the Sigmoid function.