So a couple things to address here with how you've built your LUIS model and what to expect from dispatch. Skip down to 2.) if you're a user who's reading this post and already has entities working in child LUIS models beautifully. @AlexandreViegas, read point 1.) to help properly build your LUIS model to detect intent properly in dispatch.
1. Use a Simple Entity + Phrase List to take Advantage of LUIS's machine learning--not List Entity
Right now it seems like your choice of using a list entity is not the best way to go here, and not how it's intended to be used. Instead list entities are used for terms that might have multiple ways of referring to the same thing.
Examples of When You Would Want to Use a List Entity
For example, California, Cali, CA, and The Golden State are all terms that refer to the same thing (a state). You can create a "States" list entity, include all 50 U.S. states and their nicknames. Now since this is a closed, explicit list, there is no machine learning when you use a list entity--LUIS will only detect "States" list entity if there's an exact text match.
Another example of when you would want to use list entities would be say with "Departments" for a school. You could have "chemistry", "CHEM142", "chem", etc. all meant to refer to that specific department, and do so with the rest of the departments in the school.
Why you want to use a Simple Entity and add a Phrase List
You can refer to this other StackOverflow answer I wrote, regarding how to create a simple entity and boost the signal of the entity using a phrase list.
To not completely duplicate the answer given in the link above, in essence, you want to use a simple entity, so LUIS can properly predict terms as CellPhoneType entity, even though you did not explicitly include it in your model.
For example you could have a Phone intent with utterances labeling various words as CellPhoneIntententity.
When I go to the Test panel, I type in "sunflower" and "moonstone" as made up mobile phones (maybe some phone company in the future creates phones with these names as their models):
Above you can see LUIS correctly predicts Phone intent and correctly extracts sunflower and moonstone as CellPhoneType entities.
However if I enter in brand names of mobiles that don't exist in the English language--for example Blackberry's "Z3" or T-Mobile's "G2X", LUIS cannot detect this with our model as is right now. (See 2 most recent utterances).
Above you can see utterances "i'd like to order a z3" and "my g2x is broken" do not properly predict as Phone intent, nor do z3 or g2x get detected as CellPhoneType entity. This is where phrase lists come in. As specified in the docs, phrase lists are good for boosting the signal of what a cell phone type may look like, as well as adding proprietary or foreign words to your LUIS model, such as the "made-up" words of many cell phone models. Again, refer to the StackOverflow answer I linked to, if you need guidance on how to create a phrase list.
After adding different names of cell phone models to phrase list
2. Query the endpoint of the LUIS model that was created by dispatch directly
Clarification:
- When you add a child LUIS model to dispatch, even if that child LUIS model has entities in it, it will not show up in the model of the parent LUIS model created by dispatch.
the exception to the above bullet would be if you labelled an entity in a pattern
Why entities do not need to be labelled in the parent LUIS model, is because when you call the endpoint of the parent LUIS model, it does sort of a shared call, under the hood, so it doesn't have to ping LUIS twice.
You see the entities labelled from the child LUIS model in the connectedServiceResult property
How to extract entities from child LUIS model, using your parent dispatch LUIS app
Make sure to publish both the child LUIS app and the parent dispatch app.
Going to your parent dispatch-created LUIS app, go to Manage > Keys and Endpoints > click "Endpoint" to open a browser tab where your can query the parent app in the URL after q=
type in your utterances in the URL, after q= to see the entities and intents extracted from the child LUIS model under connectedServiceResult
https://westus.api.cognitive.microsoft.com/luis/v2.0/apps/xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx?verbose=true&timezoneOffset=-360&subscription-key=b7xxxxxxxxxxxxxxxxxxxxxxxxxxxx67&q=my%20iphone%20is%20broken
{
"query": "my iphone is broken",
"topScoringIntent": {
"intent": "l_Reminders",
"score": 0.99594605
},
"intents": [
{
"intent": "l_Reminders",
"score": 0.99594605
},
{
"intent": "None",
"score": 0.002990469
}
],
"entities": [],
"connectedServiceResult": {
"query": "my iphone is broken",
"topScoringIntent": {
"intent": "Phone",
"score": 0.9658808
},
"intents": [
{
"intent": "Phone",
"score": 0.9658808
},
{
"intent": "Calendar.Add",
"score": 0.0142210266
},
{
"intent": "Calendar.Find",
"score": 0.0112086516
},
{
"intent": "None",
"score": 0.009813501
},
{
"intent": "Email",
"score": 0.0025855056
}
],
"entities": [
{
"entity": "iphone",
"type": "CellPhoneType",
"startIndex": 3,
"endIndex": 8,
"score": 0.998970151
}
]
}
}
Above you can see that the parent LUIS app created from dispatch properly identifies iphone from the utterance my iphone is broken as a CellphoneType entity.
Note: you will not see results from the child LUIS model in the Test panel of the parent dispatch, because the UI does not show connectedServiceResult
