Replicating Complex Dialogue Policy of Humans via Offline Imitation Learning with Supervised Regularization
This work addresses the challenge of covariate shift in dialogue policy learning for developers of task-oriented systems, offering an incremental improvement over supervised and reinforcement learning approaches.
The study tackled the problem of imitating complex human dialogue policies in task-oriented systems by proposing an offline imitation learning model with supervised regularization, which outperformed existing methods on action prediction across four public datasets.
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. Performances of SL-based models are restricted because of the covariate shift problem. Specifically, a dialogue is a sequential decision-making process where slight differences in current utterances and actions will cause significant differences in subsequent utterances. Therefore, the generalize ability of SL models is restricted because statistical characteristics of training and testing dialogue data gradually become different. This study proposed an offline imitation learning model that learns policy from real dialogue datasets and does not require user simulators. It also utilizes state transition information, which alleviates the influence of the covariate shift problem. We introduced a regularization trick to make our model can be effectively optimized. We investigated the performance of our model on four independent public dialogue datasets. The experimental result showed that our model performed better in the action prediction task.