  There is an increasing demand for task-oriented dialogue systems which can
assist users in various activities such as booking tickets and restaurant
reservations. In order to complete dialogues effectively, dialogue policy plays
a key role in task-oriented dialogue systems. As far as we know, the existing
task-oriented dialogue systems obtain the dialogue policy through
classification, which can assign either a dialogue act and its corresponding
parameters or multiple dialogue acts without their corresponding parameters for
a dialogue action. In fact, a good dialogue policy should construct multiple
dialogue acts and their corresponding parameters at the same time. However,
it's hard for existing classification-based methods to achieve this goal. Thus,
to address the issue above, we propose a novel generative dialogue policy
learning method. Specifically, the proposed method uses attention mechanism to
find relevant segments of given dialogue context and input utterance and then
constructs the dialogue policy by a seq2seq way for task-oriented dialogue
systems. Extensive experiments on two benchmark datasets show that the proposed
model significantly outperforms the state-of-the-art baselines. In addition, we
have publicly released our codes.
