  Persuasion dialogue systems reflect the machine's ability to make strategic
moves beyond verbal communication, and therefore differentiate themselves from
task-oriented or open-domain dialogue systems and have their own unique values.
However, the repetition and inconsistency problems still persist in dialogue
response generation and could substantially impact user experience and impede
the persuasion outcome. Besides, although reinforcement learning (RL)
approaches have achieved big success in strategic tasks such as games, they
require a sophisticated user simulator to provide real-time feedback to the
dialogue system, which limits the application of RL on persuasion dialogues. To
address these issues towards a better persuasion dialogue system, we apply RL
to refine a language model baseline without user simulators, and distill
sentence-level information about repetition, inconsistency, and task relevance
through rewards. Moreover, to better accomplish the persuasion task, the model
learns from human demonstration to imitate human persuasion behavior and
selects the most persuasive responses. Experiments show that our model
outperforms previous state-of-the-art dialogue models on both automatic metrics
and human evaluation results on a donation persuasion task, and generates more
diverse, consistent and persuasive conversations according to the user
feedback.
