Well your concern about duel or multi intents with NLU training data is right, many of us think about that, but I’d recommend you if it’s necessary or you have big data with common intents then only try this use case else stick with generic approach and even you should be very specific about your intent selection and training examples for the same. I hope it make sense.
For your understanding please see this example:
NLU training data
What does the training data look like for models using the TensorFlow pipeline? Not that different from the regular approach - the only addition is that we have to add examples of multi-intent inputs and assign them the corresponding multi-intent labels. Below I have a snippet of training data which I am going to use to train the NLU model (check the
data/nlu_data.md file). As you can see, I have some regular examples with one intent per input as well as examples which have multiple intents assigned. For example, the input “Can you suggest any cool meetups in Berlin area?” has only one intention - the user asks for meetup recommendations, that’s why it has a single intent assigned to it. On the flipside, the input “Sounds good. Do you know how I could get there from home?” means two things - confirmation that a user wants to join the meetup and a query about the transport to get to the venue, and this is why such examples have a combined
## intent: meetup
- I am new to the area. What meetups I could join in Berlin?
- I have just moved to Berlin. Can you suggest any cool meetups for me?
## intent: affirm+ask_transport
- Yes. How do I get there?
- Sounds good. Do you know how I could get there from home?
Reference link for more details : How to Handle Multiple Intents Using Rasa NLU TensorFlow Pipeline
I hope this will clear your concern and solve your issue. Good Luck!