u t can then be represented as an attention vector l t computed as follows In contrast to the traditional attention mechanism (Bahdanau et al., 2015), which only depends on one set of hidden vectors from the Seq2Seq decoder, the DA attention of our model relies on two sets of hidden vectors, i.e., g t−1 of the conversation-level DA tagger and s t−1 of the conversation-level topic tagger, where dual attention mechanism can capture, for utterances, information about both DAs and topics as well as the interaction between them.
the weights for the DA attention are calculated as follows: The topic attention layer has a similar architecture to the DA attention layer, which takes as input both s t−1 and g t−1 .