Hi all, I’m getting very strange answers with my Bot, it seems like stories are not well taken into account.
Here is my config
pipeline: supervised_embeddings language: fr policies:
- name: MemoizationPolicy max_history: 1
- name: KerasPolicy
- name: MappingPolicy
- name: FallbackPolicy nlu_threshold: 0.3 core_threshold: 0.1 fallback_action_name: action_default_fallback
In the stories I have for example :
rervation voiture
- reservation{“reservable”:“voiture”}
- utter_deplacements
navette
- reservation{“reservable”:“navette”}
- utter_reservation_navette
And for example with interactive learning here is the result :
New Story
- reservation{“reservable”:“voiture”}
- utter_reservation_salle
Which should trigger utter_deplacements and not utter_reservation_salle as the training stories specify it. And every time I have training data with a different answer depending on the entity the answer is never the good one (not the one specified in the training data) and seems to be choosen randomly (even with twice the same example). And when I manage to make it choose the right answer for one entity, it does not work anymore with others.
What should I do ? Is there a configuration misunderstanding on my side ? Thanks a lot for any feedback