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.
@article{arxiv.1906.00549,
title = {Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment},
author = {Siqi Bao and Huang He and Fan Wang and Rongzhong Lian and Hua Wu},
journal= {arXiv preprint arXiv:1906.00549},
year = {2019}
}