Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration. (arXiv:2012.15375v2 [cs.CL] UPDATED)

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 …

arxiv human persuasion reinforcement reinforcement learning