  In this paper, a novel Generation-Evaluation framework is developed for
multi-turn conversations with the objective of letting both participants know
more about each other. For the sake of rational knowledge utilization and
coherent conversation flow, a dialogue strategy which controls knowledge
selection is instantiated and continuously adapted via reinforcement learning.
Under the deployed strategy, knowledge grounded conversations are conducted
with two dialogue agents. The generated dialogues are comprehensively evaluated
on aspects like informativeness and coherence, which are aligned with our
objective and human instinct. These assessments are integrated as a compound
reward to guide the evolution of dialogue strategy via policy gradient.
Comprehensive experiments have been carried out on the publicly available
dataset, demonstrating that the proposed method outperforms the other
state-of-the-art approaches significantly.
