  Dialogue policy learning, a subtask that determines the content of system
response generation and then the degree of task completion, is essential for
task-oriented dialogue systems. However, the unbalanced distribution of system
actions in dialogue datasets often causes difficulty in learning to generate
desired actions and responses. In this paper, we propose a
retrieve-and-memorize framework to enhance the learning of system actions.
Specially, we first design a neural context-aware retrieval module to retrieve
multiple candidate system actions from the training set given a dialogue
context. Then, we propose a memory-augmented multi-decoder network to generate
the system actions conditioned on the candidate actions, which allows the
network to adaptively select key information in the candidate actions and
ignore noises. We conduct experiments on the large-scale multi-domain
task-oriented dialogue dataset MultiWOZ 2.0 and MultiWOZ 2.1. Experimental
results show that our method achieves competitive performance among several
state-of-the-art models in the context-to-response generation task.
