I’ve been wondering. Didn’t check/tested the TensorFlow components of the source code, but maybe someone here could give me a light avoiding my need to check things manually.
I have this modelling doubt about multiple intents:
How does declaring instances on a multiple intent class on nlu.md differs from declaring a whole new class? Is just a matter of control/syntax or the classification performance does improve? Most important thing is: does multiple intent uses the marginal information from the intents that compose it? If I activate the multiple intent funcionality, the NLU will label something as A + B even with only A instances and B instances on the trainset?
If there are indeed multiple labels (in terms of classification) instead of a new class, how does the validation/test metrics are affected when declaring the use of multiple intents?
Any help on this would be very much appreciated.