A Corpus-free State2Seq User Simulator for Task-oriented Dialogue
Table 5: Analysis of agent policy performance during training on movie domain. At beginning of each policy training epoch, we test the policy’s performance. The results reflect the models’ overfitting to training set.
['Model', 'Succ.', 'Avg. Rwd.', 'Avg. Turns']
We perform testing at each training epoch, and report the averaged score. The policy is evaluated against the environment for training. Policy trained by our model outperforms the ones from statistical user simulator, which reflects that our user simulator improves the training performance of agent policy. It is because our user simulator has better generalization to respond to agent’s unreasonable actions in the early training stage. On the other hand, diversity helps RL algorithm training. Agenda achieve good scores as rule environment is relatively easy for overfitting.