I have read the embedding policy paper. This model is powerful due to transfer learning. Furthermore, it is able to rollback to a specific context using the attention mechanism.
However, I am not sure whether this model is necessary to build the goal oriented dialogue system. Rule-based approach, such as the form policy and memorization policy, is almost enough to build all goal oriented dialogue system.
I have checked the training data and testing data in your open source. (GitHub - RasaHQ/conversational-ai-workshop-18: Example showing generalisation) I think all dialogue patterns in this data can be handled according to the following rules.
- When AI requests a slot, user answer it. -> AI requests another slot or provides the final action.
- When AI requests a slot, user answer another message.(unhappy path) -> AI must respond to that message and then request the slot.
It is very simple… I do not know why dialogue data should be necessary for training embedding policy.
I would like to know an example story that embedding policy can handle and the rule based approach can not handle.
Thanks is advance