Replicating Complex Dialogue Policy of Humans via Offline Imitation Learning with Supervised Regularization

Understand

Policy learning (PL) is a module of a task-oriented dialogue system that trains an agent to make actions in each dialogue turn.

• Imitating human action is a fundamental problem of PL.
• However, both supervised learning (SL) and reinforcement learning (RL) frameworks cannot imitate humans well.
• Training RL models require online interactions with user simulators, while simulating complex human policy is hard.