Runzhe Yang

  • Citations Per Year
Learn More
On-line dialogue policy learning is the key for building evolvable conversational agent in real world scenarios. Poor initial policy can easily lead to bad user experience and consequently fail to attract sufficient real users for policy training. We propose a novel framework, companion teaching, to include a human teacher in the on-line dialogue policy(More)
Hand-crafted rules and reinforcement learning (RL) are two popular choices to obtain dialogue policy. The rule-based policy is often reliable within predefined scope but not self-adaptable, whereas RL is evolvable with data but often suffers from a bad initial performance. We employ a companion learning framework to integrate the two approaches for on-line(More)
  • 1