Memory-Augmented Dialogue Management for Task-Oriented Dialogue Systems

Abstract

Dialogue management (DM) is responsible for predicting 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 DM requires having access not only to local utterances but also to the global semantics of the entire dialogue session, modeling the long-range history information is a critical issue. To this end, we propose MAD, a novel memory-augmented dialogue management model that 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 (i.e., cuisine, price, and location), and the external memory augments the representation of hidden states of traditional recurrent neural networks by 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.