Multi-action dialog policy (MADP), which generates multiple atomic dialog
actions per turn, has been widely applied in task-oriented dialog systems to
provide expressive and efficient system responses. Existing MADP models usually
imitate action combinations from the labeled multi-action dialog samples. Due
to data limitations, they generalize poorly toward unseen dialog flows. While
interactive learning and reinforcement learning algorithms can be applied to
incorporate external data sources of real users and user simulators, they take
significant manual effort to build and suffer from instability. To address
these issues, we propose Planning Enhanced Dialog Policy (PEDP), a novel
multi-task learning framework that learns single-action dialog dynamics to
enhance multi-action prediction. Our PEDP method employs model-based planning
for conceiving what to express before deciding the current response through
simulating single-action dialogs. Experimental results on the MultiWOZ dataset
demonstrate that our fully supervised learning-based method achieves a solid
task success rate of 90.6%, improving 3% compared to the state-of-the-art
methods.

本文提出了一种基于多任务学习框架的 Planning Enhanced Dialog Policy (PEDP) 方法，使用模型规划来模拟单动作对话，从而增强多动作预测，实现了相对于现有状态下最先进方法的 3% 提高，达到了 90.6% 的可靠任务成功率。