How to set up slots with AMAZON.StartOverIntent utterance?
Ex: I want to start the skill with a custom slot value as in Alexa, ask <my skill> the definition of <custom value>
I read that AMAZON.StartOverIntent cannot have custom slot so I broke it like this:
DefIntent {Term}
AMAZON.StartOverIntent the definition of
AMAZON.StartOverIntent define
AMAZON.StartOverIntent what is
That doesn't seem to work when I test it with Echo. How do you go about declaring such utterance?
Why are you trying to override StarOverIntent? The normal way to do things is to use your own intents. You only need to use the built in intents if you want to. And, even then, it is just a short cut. You still have to implement them. They don't actually come with in built in functionality.
For what you want, you can declare the following intent:
{
"intents":[
{
"intent":"DefIntent",
"slots":[
{
"name":"term",
"type":"TERM"
}
]
}
]
}
This creates one intent with one slot which is a custom type TERM. You can create the list of terms you want to look up in plan text file and upload it as the values for that custom type. You can then declare utterances:
DefIntent the definition of {term}
DefIntent define {term}
DefIntent what is {term}
That should give you what you want.
Or close to what you want. I imagine you want the user to be able to say anything at all for {term}. But Alexa is not a dictation machine. It doesn't work that way. It expects a moderately restrictive vocabulary in order to produce the highest quality recognition.
You can fake it out, by providing a custom list with a hundred thousand words in it. Or other techniques to create a "generic slot". But it will not perform with a high quality of recognition. You are better off re-thinking your design so that you don't need generic dictation.
For a fully worked, complex example of an Alexa skill, with nearly an hour of video, see SubWar.
Related
I have an Alexa skill that at one point asks for names, and at another point asks for numbers. The names are being mapped to a slot of type AMAZON.FirstName and the numbers to a slot of type AMAZON.NUMBER. The problem is that Alexa is aggressively interpreting even number values as names. (e.g. Saying "eight" is likely to be cast as the name "Tate.")
From what I can tell, Dialog Delegation is useful only if you know exactly how many of each type you need to capture. But in my case there are a variable number of times I will need to capture a name, so I can't just fill that slot once and be done with it.
Ideally I would like a way to programmatically turn the slots on and off. So when I prompt the user for a name, any utterance CAN ONLY BE MAPPED TO A NAME or else rejected (obviously HELP and EXIT, etc would still work). And then when I ask for a number, any utterance WILL ONLY BE MAPPED TO A NUMBER, it won't even try to cast it into the type AMAZON.FirstName.
Is there any way to achieve that? Or are there any other workarounds for scenarios like this?
I'd change the approach you're taking. You have a great tool for validation before even getting to code itself. Click the slot you're trying to validate, then click on the "validations" tab.
Right there, you can add either one or two rules. If you do one, you can add the "not within a set of values" and you can type "one", "two", etc. in order to avoid getting those numeric values inside your name slot.
If you take the two rule validation, you will need to add a "Value within slot types' slot values". That way, you will only accept values inside the AMAZON.FirstName slot type.
You don't really need to enable/disable a slot, you can simply take both in the same utterance. Just make sure you're validating your slots properly and that way you'll avoid unvalid data from getting into your skill :)
Read more: https://developer.amazon.com/es-mx/docs/custom-skills/validate-slot-values.html
I am developing an app and everything is working good. One condition are there where I have set the utterances but if user is speaking something else, I am throwing it to the fallbackIntent. One of my utterance is {name} so user can speak any name. But I have define range of name as well that user is allowed only these names. So my problem is if the user is choosing defined names, everything working great and if user said something else like what is weather of chicago, it is going to fallbackIntent as well but the issue is if user speak some name which is not in the list, then too it is coming into defined intent. What i want that if user speak something which is correct but not in my defined name then too redirect it to the fallbackIntent. Is there any way I can call intent in giving condition? I am using php.
When you define a custom slot, Alexa take it's values as samples. So values which are not in the slot-value-list will also be passed to you. And with respect your intent, those slot values are valid, hence that intent is triggered.
The solution is to validate the slot values at your backend and return an appropriate response.
In your case, if u get any other names other than those you have defined, respond back with an error or give FallbackIntent's response.
When you create a custom slot type, a key concept to understand is
that this is training data for Alexa’s NLP (natural language
processing). The values you provide are NOT a strict enum or array
that limit what the user can say. This has two implications
1) words and phrases not in your slot values will be passed to you,
2) your code needs to perform any validation you require if what’s
said is unknown.
I am creating a skill but I need a slot type for my intent (which takes a complete sentence as input) but it should be in Indian.
Like: AMAZON.LITERAL
It only supports English(U.K) and English(US).
I need any slot type which takes a complete sentence as input but supports English(Indian). Thanks.
I'm also seraching for this one for last 2 weeks. And also i email to support team of alexa. After that i got confirmation from support team that AMAZON.LITERAL slot type is not support feature in english(india).
You can only fake it:
create your own slot type
for this slot type give a lot of different utterances in different length (like "one", "this is a sentence.." and so on)
use your slot with custom slot type in your utterances
I also needed it for a simple echo skill in GERMAN which just reply.
See the dialogue model here:
https://gitlab.com/timguy/alexa-wiederhall/blob/master/src/main/java/github/timguy/wiederhall/speechAssets/IntentSchemaSlotsUtterances_SkillBuilder.json
instead of samples "{slotOne}" you could use " save the text I say {slotOne}"
UPDATE
I didn't tried it but just read it now. Now you could use AMAZON.SearchQuery
https://developer.amazon.com/de/blogs/alexa/post/a2716002-0f50-4587-b038-31ce631c0c07/enhance-speech-recognition-of-your-alexa-skills-with-phrase-slots-and-amazon-searchquery
Amazon.Literal is deprecated and if you are changing the skill to en-Us then it won't be available in any other locale which i think you never want.
Moreover as per my interaction with amazon development team it is not a good option to have a free text in terms of Amazon.Literal as it would add more ambiguity in their NLP for resolving intent and slot than addressing the underlying issue. Since Alexa doesn't provide you any confidence factor in intent/slot then it would be a big problem to your skill as any random word/sentence would match to Amazon.Literal.
It's always good to restrict your user input as you are developing the skill especially when it involves AI/NLP.
Update
You can use the new slot type Amazon.SearchQuery that would suit your problem
Can any one tell me the slot type of the intent which can have long sentences in the English(India) Language? In English(US) I use AMAZON.StreetAddress for this purpose. Thanks.
Consider using a custom slot type. According to Amazon, use of the Amazon.LITERAL type is discouraged, and a custom slot type is recommended instead. Usually for custom slot types, you specify a set of sample values. However, based on your use case, it sounds like you want a match that is as close as possible to just catching all possible inputs, which is Scenario #3 from this Amazon Alexa blog article. As the article's content for the "Catch All" scenario mentions:
If you use the same training data that you would have used for
LITERAL, you’ll get the same results.
IMO, of special importance is the last paragraph regarding Scenario #3.
If you’re still not getting the results, trying setting the CatchAll
values to around twenty 2 to 8 word random phrases (from a random word
generator – be really random). When the user says something that
matches your other utterances, those intents will still be sent. When
it doesn’t match any of those, it will fall to the CatchAll slot. If
you go this route, you’re going to lose accuracy because you’re not
taking full advantage of Alexa’s NLP so you’ll need to test heavily.
Hope that helps.
For example: If the user writes in the Watson Conversation Service:
"I wouldn't want to have a pool in my new house, but I would love to live in a Condo"
How you can know that user doesn't want to have a pool, but he loves to live in a Condo?
This is a good question and yeah this is a bit tricky...
Currently your best bet is to provide as much examples of the utterances that should be classified as a particular intent as a training examples for that intent - the more examples you provide the more robust the NLU (natural language understanding) will be.
Having said that, note that using examples such as:
"I would want to have a pool in my new house, but I wouldn't love to live in a Condo"
for intent-pool and
"I wouldn't want to have a pool in my new house, but I would love to live in a Condo"
for intent-condo will make the system to correctly classify these sentences, but the confidence difference between these might be quite small (because of the reason they are quite similar when you look just at the text).
So the question here is whether it is worth to make the system classify such intents out-of-the-box or instead train the system on more simple examples and use some form of disambiguation if you see the top N intents have low confidence differences.
Sergio, in this case, you can test all conditions valid with peers node (continue from) and your negative (example else) you can use "true".
Try used the intends for determine the flow and the entities for defining conditions.
See more: https://www.ibm.com/watson/developercloud/doc/conversation/tutorial_basic.shtml
PS: you can get the value of entity using:
This is a typical scenario of multi intents in Conversation service. Everytime user says something, all top 10 intents are identified. You can change your dialog JSON editor like this to see all intents.
{
"output": {
"text": {
"values": [
"<? intents ?>"
],
"selection_policy": "sequential"
}
}
}
For example, When user makes a statement, that will trigger two intents, you'll see that intents[0].confidence and intents[1].confidence both will be pretty high, which means that Conversation identified both the intents from the user text.
But there is a major limitation in it as of now, there is no guaranteed order for the identified intents, i.e. if you have said
"I wouldn't want to have a pool in my new house, but I would love to live in a Condo", there is no guarantee that positive intent "would_not_want" will be the intents[0].intent and intent "would_want" will be the intents[1].intent. So it will be a bit hard to implement this scenario with higher accuracy in your application.
This is now easily possible in Watson Assistant. You can do this by creating contextual entities.
In your intent, you mark the related entity and flag it to the entity you define. The contextual entities will now learn the structure of the sentence. This will not only understand what you have flagged, but also detect entities you haven't flagged.
So example below ingredients have been tagged as wanted and not wanted.
When you run it you get this.
Full example here: https://sodoherty.ai/2018/07/24/negation-annotation/