  Dialogue management (DM) decides the next action of a dialogue system
according to the current dialogue state, and thus plays a central role in
task-oriented dialogue systems. Since dialogue management requires to have
access to not only local utterances, but also the global semantics of the
entire dialogue session, modeling the long-range history information is a
critical issue. To this end, we propose a novel Memory-Augmented Dialogue
management model (MAD) which employs a memory controller and two additional
memory structures, i.e., a slot-value memory and an external memory. The
slot-value memory tracks the dialogue state by memorizing and updating the
values of semantic slots (for instance, cuisine, price, and location), and the
external memory augments the representation of hidden states of traditional
recurrent neural networks through storing more context information. To update
the dialogue state efficiently, we also propose slot-level attention on user
utterances to extract specific semantic information for each slot. Experiments
show that our model can obtain state-of-the-art performance and outperforms
existing baselines.
