Create an Intent

Here are the steps for creating an intent in a skill.

To create an intent:

Click Intents in the left navbar.
If you already have defined your intents in a CSV file, click Import Intents. Import Intents from a CSV File describes this file's format. Otherwise, click Add Intent. Your skill needs at least two intents.
Click to enter a descriptive name or phrase for the intent in the Conversation Name field. For example, if the intent name is callAgent, the conversation name would be Talk to a customer representative. When the skill can't resolve a message to an intent, it outputs the user-friendly names and phrases that you enter into the Conversation Name field as the options that are listed in the Do you want to disambiguation messages described in How Confidence Win Margin Works and Configure the Intent and Q&A Routing.
Add the intent name in the Name field. If you don't enter a conversation name, then the Name field value is used instead. Keep in mind that a short name with no end punctuation might not contribute to the user experience. The intent name displays in the Conversation Name field for skills built with prior versions of Digital Assistant.

Note

In naming your intents, do not use system. as a prefix. system. is a namespace that's reserved for the intents that we provide. Because intents with this prefix are handled differently by Trainer Tm, using it may cause your intents to resolve in unexpected ways.
Add a description of the intent. Your description should focus on what makes the intent unique and the task or actions it performs.
If this is an answer intent, add a short answer to the Answer field.
Optionally, in the Annotations field, add one or more tags for the intent to categorize it in a way that is useful for you. You can use any words of your choosing.

Tip:
On the Intents page, you can filter the display of intents by annotation.
Start building the training corpus by adding example utterances that illustrate the meaning behind the intent. To ensure optimal intent resolution, use terms, wording, and phrasing specific to the individual intent. Ideally, you should base your training data on real-world phrases. You can save your utterances by clicking Enter or by clicking outside of the input field. To manage the training set, select a row to access the Edit () and Delete () functions.
If your skill supports multiple native languages, augment the training set with phrases in the secondary languages to strengthen the model's accuracy in this and all other native languages supported by the skill.

See Build Your Training Corpus for tips on building an effective training corpus.

To allow your skill to cleanly distinguish between intents, create an intent that resolves inappropriate user input or gibberish.

While utterances can be added to an existing intent manually or by importing a CSV, they can also be assigned to intents through data manufacturing jobs and the Insights retrainer.
In the Auto-Complete Suggestions field, enter a set of suggested phrases that help the user enter an appropriately worded request. Do not add the entire set of training data. Add a set of phrases that represent ideal user requests instead. Adding too broad a set of utterances may not only confuse users, but may also result in unexpected behavior.
This is an optional step. This function is only supported by the Oracle Web Channel.
Add an entity if the intent needs one to resolve the user input. To find out how, see Add Entities to Intents.
To teach your skill how to comprehend user input using the set of utterances that you’ve provided so far, click Train, choose a model and then click Submit.
As described in Which Training Model Should I Use?, we provide two models that learn from your corpus: Trainer Ht and Trainer Tm. Each uses a different algorithm to reconcile the user input against your intents. Trainer Ht uses pattern matching while Trainer Tm a machine learning algorithm which uses word vectors. Both skills that use Digital Assistant's native language support and skills with answer intents require Trainer TM.
You’d typically follow this process:
1. Create the initial training corpus.
2. Train with Trainer Ht. You should start with Trainer Ht because it doesn’t require a large set of utterances. As long as there are enough utterances to disambiguate the intents, your skill will be able to resolve user input.
  
  If you get a Something’s gone wrong message when you try to train your skill, then you may not have added a sufficient number of utterances to support training. First off, make sure that you have at least two intents with at least two (or preferable more) utterances each. If you haven’t added enough utterances, add a few more then train your skill.
3. Refine your corpus, retrain with Trainer Ht. Repeat as necessary—training is an iterative process.
4. Train with Trainer Tm. Use this trainer when you’ve accumulated a robust set of intents.
The Training Needed displays whenever you add an intent or when you update an intent by adding, changing, or deleting its utterances. To bring the training up to date, choose a training model and then click Train. The model displays an exclamation point whenever it needs training. When its training is current, it displays a check mark.
Click Test Utterances (located at the upper left) to open the Utterance Tester. Select the target language, then enter utterances similar to those in your training set. The Utterance Tester returns the confidence level for this utterance and enables you to assign the utterance to an intent, or add it as a test case.
To log your intent testing results, enable the conversation intent logging (Settings > General > Enable Insights) .
Click Validate and review the validation messages for errors such as too few utterances and for guidance on applying best practices like adding an unresolvedIntent intent.

Add Entities to Intents

Some intents require entities—both built-in and custom— to complete an action within the dialog flow or make a REST call to a backend API. The system uses only these entities, which are known as intent entities, to fulfill the intent that’s associated with them. You can associate an entity to an intent when you click Add New Entity and then select from the custom (

) or built-in (

) entities. If you're assigning a built-in entity, leave Value Agnostic enabled (the default) if specific entity values do not factor into intent classification (which is generally the case). If the intent requires a specific entity value, switch this feature off.

Note

Value Agnostic applies to built-in entities only. You cannot apply it to custom entities.

Description of choose_req_entity.png follows

Alternatively, you can click New Entity to add an intent-specific entity.

Description of create_intent_entity.png follows

Tip:

Only intent entities that are included in the JSON payloads are sent to, and returned by, the Component Service. The ones that aren’t associated with an intent won’t be included, even if they contribute to the intent resolution by recognizing user input. If your custom component accesses entities through entity matches, then be sure to add the entity to your intent.

Value Agnostic Intent Entities

The Value Agnostic feature allows you to adjust how entity values affect intent classification. When you enable this feature, the specific values for an associated built-in entity do not have bearing on the intent classification. However, when you disable this feature, you allow the entity value to play a key role in resolving the input.

In general, you can leave this feature in its default setting (enabled) because a specific entity value seldom factors into intent classification. The training utterances for an account balances intent, for example, may include specific dates (What was my balance on October 5?) but these values are not the deciding factor in resolving the input to the intent. Leaving Value Agnostic enabled will, in most cases, improve intent resolution because it prevents the values from affecting confidence scores or even signaling an unintended intent. However, whenever specific values play a key role in intent resolution, you should switch this feature off. For example, you would disable the feature if the value for a DATE is central to distinguishing an intent for checking past vacation balances from an intent that checks for future vacation balances. If these intents were date agnostic, then the model would ignore past and present and would not resolve input correctly.

Example Intents	Associated Entity	Training Utterances	Enable Value Agnostic?
Account Balance	DATE	Can you tell me my account balance yesterday? How much money do I have in checking? What was my balance on October 5th? What was my credit card balance last week? What is my bank balance today? What was my savings account balance on 5/3?	Yes – The specific date values do not signal the intent. The various date values in these utterances can be ignored because a user can ask for an account balance on any day.
Holiday Store Hours	DATE	Are you open on January 1st? Are you open on Thanksgiving? Hours for New Year's Day What are the store hours for July 4th? What are your holiday hours? Will you be open on Christmas?	No – The intent classification is based on a specific (and limited) set of values and users are inquiring about holidays.
Check Past Vacation Balance Check Future Vacation Balance	DATE	Check Past Vacation Balance Did I take any time off last month? Check Future Vacation Balance Any planned vacation in next month?	No – Disable Value Agnostic for both intents. Agnostic DATE values in this case would mean that the model would not consider a value as past or future. A "last month" value, which should signal the Check Past Vacation Balance intent, would be ignored. As a result, similarly worded input like "Did I take any time off next month" may resolve incorrectly to this intent.

Import Intents from a CSV File

You can add your intents manually, or import them from a CSV file. You can create this file from a CSV of exported intents, or by creating it from scratch in a spreadsheet program or a text file.

The CSV file has six columns for skills that use the Natively-Supported language mode and five columns for those that don't. Here are the column names and what they represent:

query: An example utterance.
topIntent: The intent that the utterance should match to.
conversationName: The conversation name for the intent.
answer: For answer intents, the static answer for the intent.
enabled: If true, the intent is enabled in the skill.
nativeLanguageTag: (For skills with native-language support only) the language of the utterance. For values, use two-character language tags (fr, en, etc,).
- For skills with Digital Assistant's native language support, this column is required.
- For skills without the native language support, you can't import a CSV that has this column.

Here's an excerpt from a CSV file for a skill that does not have native language support and which doesn't use answer intents.

query,topIntent,conversationName,answer,enabled
I want to order a pizza,OrderPizza,Order a Pizza.,,true
I want a pizza,OrderPizza,Order a Pizza.,,true
I want a pizaa,OrderPizza,Order a Pizza.,,true
I want a pizzaz,OrderPizza,Order a Pizza.,,true
I'm hungry,OrderPizza,Order a Pizza.,,true
Make me a pizza,OrderPizza,Order a Pizza.,,true
I feel like eating a pizza,OrderPizza,Order a Pizza.,,true
Gimme a pie,OrderPizza,Order a Pizza.,,true
Give me a pizza,OrderPizza,Order a Pizza.,,true
pizza I want,OrderPizza,Order a Pizza.,,true
I do not want to order a pizza,CancelPizza,Cancel your order.,,true
I do not want this,CancelPizza,Cancel your order.,,true
I don't want to order this pizza,CancelPizza,Cancel your order.,,true
Cancel this order,CancelPizza,Cancel your order.,,true
Can I cancel this order?,CancelPizza,Cancel your order.,,true
Cancel my pizza,CancelPizza,Cancel your order.,,true
Cancel my pizaa,CancelPizza,Cancel your order.,,true
Cancel my pizzaz,CancelPizza,Cancel your order.,,true
I'm not hungry anymore,CancelPizza,Cancel your order.,,true
don't cancel my pizza,unresolvedIntent,unresolvedIntent,,true
Why is a cheese pizza called Margherita,unresolvedIntent,unresolvedIntent,,true

Here's an excerpt from a CSV file for a skill with native-language support that uses answer intents.

query,topIntent,conversationName,answer,enabled,nativeLanguageTag
Do you sell pasta,Products,Our Products,We sell only pizzas. No salads. No pasta. No burgers. Only pizza,true,en
Vendez-vous des salades,Products,Our Products,Nous ne vendons que des pizzas. Pas de salades. Pas de pâtes. Pas de hamburgers. Seulement pizza,fr
do you sell burgers,Products,Our Products,We sell only pizzas. No salads. No pasta. No burgers. Only pizza,true,en
Do you sell salads,Products,Our Products,We sell only pizzas. No salads. No pasta. No burgers. Only pizza,true,en
Vendez des hamburgers,Products,Our Products,Nous ne vendons que des pizzas. Pas de salades. Pas de pâtes. Pas de hamburgers. Seulement pizza,true,fr

To import a CSV file:

Click Intents () in the left navbar.
Click More, and then choose Import intents.
Select the .csv file and then click Open.
Train your skill.

Export Intents to a CSV File

You can reuse your training corpus by exporting it to CSV. You can then import this file to another skill.

To export your intents and their utterances:

Click Intents in the left navbar.
Click More, and then choose Export intents.
Save the file. This file has the following columns, which are described in Import Intents from a CSV File:
```
query, topIntent, conversationName, answer, enabled, nativeLanguageTag
```

Which Training Model Should I Use?

We provide a duo of training models that mold your skill’s cognition, Trainer Tm and Trainer Ht. You can use either of these models, each of which uses a different approach to machine learning. In general, you train your with Trainer Tm before you put your skills into production. Because of its shorter training time, you can use Ht for prototyping or for skills.

Note

You can't use Trainer Ht for skills that use answer intents, use native language support, or have a large number of intents. Use Trainer Tm for these skills.

Trainer Ht is the default model, but you can change this by clicking Settings > General and then by choosing another model from the list. The default model displays in the tile in the skill catalog.

Trainer Tm

Trainer Tm (Tm) achieves highly accurate intent classification even when a skill has hundreds, or even thousands, of intents. Even though the intents in these large data sets are often closely related and are sometimes "unbalanced" in quantity of utterances, Tm can still differentiate between them. In general, you would apply Tm to any skill before you put it into production.

Note

When you train with Trainer Tm, you can also use the Similar Utterances Report.

You don't need to bulk up your training data with utterances that accommodate case sensitivity (Tm recognizes BlacK Friday as Black Friday, for example), punctuation, similar verbs and nouns, or misspellings. In the latter case, Trainer Tm uses context to resolve a phrase even when a user enters a key word incorrectly. Here are some general guidelines for building a training corpus when you're developing your skill with this model.

Trainer Tm enhances the skill's cognition by

Recognizing the irrelevant content. For I'm really excited about the coming Black Friday deals, and can't wait for the deals. Can you tell me what's going to be on sale for Black Friday?, Trainer Tm:
- Discards the extraneous content (I'm really excited about the coming Black Friday deals...)
- Resolves the relevant content (Can you tell me what's going to be on sale for Black Friday?) to an intent. In this case, an intent called Black Friday Deals.
Trainer Tm can also distinguish between the relevant and irrelevant content in a message even when the irrelevant content can potentially be resolved to an intent. I bought the new 80 inch TV on Black Friday for $2200, but now I see that the same set is available online for $2100. Do you offer price match? for example, could be matched to the Black Friday Deals intent and to a Price Matching intent, which is appropriate for this message. In this case Trainer Tm:
- Recognizes that I bought the new 80 inch TV on Black Friday for $2200, but now I see that the same set is available online for $2100 is extraneous content.
- Resolves Do you offer price match?
Resolving intents when a single word or a name matches an entity. For example, Trainer Tm can resolve a message consisting of only Black Friday to an intent that's associated with a entity for Black Friday.
Distinguishing between similar utterances (Cancel my order vs. Why did you cancel my order?).
Recognizing out-of-scope utterances, such as Show me pizza recipes or How many calories in a Meat Feast for a skill for fulfilling a pizza order and nothing else.
Recognizing out-of-domain utterances, such as What's the weather like today for a pizza ordering skill.

Tip:
While Trainer Tm can easily distinguish when a user message is unclassifiable because it's clearly dissimilar from the training data, you might still want to define an unresolvedIntent with utterances that represent the phrases that you want to make sure do not resolve to any of your skill's intents. These phrases can be within the domain of your skill, but are still out of scope, even though they may share some of the same words as the training data. For example, I want to order a car for a pizza skill, which has also been trained with I want to order a pizza.
Distinguishing between similar entities – For example, Tm recognizes that mail is not same as email in the context of an intent called Sign Up for Email Deals. Because it recognizes that an entity called regular mail would be out of scope, it would resolve the phrase I want to sign up for deals through regular mail at a lower confidence than it would for I want to sign up for email deals.

Trainer Ht

Trainer Ht is the default training model. It needs only a small training corpus, so use it as you develop the entities, intents, and the training corpus. When the training corpus has matured to the point where tests reveal highly accurate intent resolution, you’re ready to add a deeper dimension to your skill’s cognition by training Trainer Tm.

You can get a general understanding of how Trainer Ht resolves intents just from the training corpus itself. It forms matching rules from the sample sentences by tagging parts of speech and entities (both custom and built-in) and by detecting words that have the same meaning within the context of the intent. If an intent called SendMoney has both Send $500 to Mom and Pay Cleo $500, for example, Trainer Ht interprets pay as the equivalent to send . After training, Trainer Ht’s tagging reduces these sentences to templates (Send Currency to person, Pay person Currency) that it applies to the user input.

Because Trainer Ht draws on the sentences that you provide, you can predict its behavior: it will be highly accurate when tested with sentences similar to the ones that make up the training corpus (the user input that follows the rules, so to speak), but may fare less well when confronted with esoteric user input.

Build Your Training Corpus

When you define an intent, you first give it a name that illustrates some user action and then follow up by compiling a set of real-life user statements, or utterances. Collectively, your intents, and the utterances that belong to them, make up a training corpus. The term corpus is just a quick way of saying “all of the intents and sample phrases that I came up with to make this skill smart”. The corpus is the key to your skill’s intelligence. By training a model with your corpus, you essentially turn that model into a reference tool for resolving user input to a single intent. Because your training corpus ultimately plays the key role in deciding which route the skill-human conversation will take, you need to choose your words carefully when building it.

Generally speaking, a large and varied set of sample phrases increases a model’s ability to resolve intents accurately. But building a robust training corpus doesn’t just begin with well-crafted sample phrases; it actually begins with intents that are clearly delineated. Not only should they clearly reflect your use case, but their relationship to their sample sentences should be equally clear. If you’re not sure where a sample sentence belongs, then your intents aren’t distinct from one another.

You probably have sample utterances in mind when you create your intents, but you can expand upon them by using these guidelines.

Guidelines for Trainer Tm

Use a minimum confidence threshold of 0.7 for any skill that you plan to put into production.
Use good naming conventions for your intent names so it's easy to review related intents.
As a general rule, create at least 80 to 100 utterances for each intent. Per the corpus size and shape guidelines, the minimum (through not recommended) number of utterances for an intent is two. The total number of utterances in your training set should not exceed 25,000.
If possible, use unmodified, real-word phrases that include:
- vernacular
- standard abbreviations that a user might enter ("opty" for opportunity, for example)
- non-standard names, such a product names
- spelling variants ("check" and "cheque", for example)
If you don't have any actual data, incorporate these in your own training data. Here are some pointers:
- Create fully formed sentences that mention both the action and the entity on which the action is performed.
- Try to keep the utterance length between 3 and 30 words. Utterances that are too short and lacking context can cause the model to generalize in unpredictable ways. Utterances that are too long may prevent the model from identifying the pertinent words and phrases. There can be exceptions, however, for one- or two-word utterances when they're commonly used phrases. If you expect two-word messages like order status, price check, membership info, or ship internationally) that specify both the entity and action, add them to your training data. Be sure that your sample phrases have both an action and an entity.
- Be specific. For example, What is your store phone number? is better than What is your phone number? because it enables Trainer Tm to associate a phone number with a store. As a result of this learning, it will resolve What's your mom's phone number? to a lower confidence score.
- While Trainer Tm detects out-of-scope utterances, you can still improve confidence and accuracy by creating an unresolvedIntent for utterances that are in domain but still out of scope for the skill's intents. This enables Trainer Tm to learn the boundary of domain intents. You can define an unresolvedIntent for phrases that you do not want resolved to any of your skill's intents. You may only want to define an unresolvedIntent when user messages have been resolved to a skill's intents even when they don't apply to any of them.
- Vary the words and phrases that surround the significant content as much as possible. For example, I'd like a pizza, please", "Can you get me a pizza?", "A pizza, please"
- Some practices to avoid:
  - Do not associate a single word or phrase with a specific intent unless that word or phrase indicates the intent. Repeated phrases can skew the intent resolution. For example, starting each OrderPizza utterance with "I want to …" and each ShowMenu intent with "Can you help me to …" may increase the likelihood of the model resolving any user input that begins with "Can you help me to" with OrderPizza and "I want to" with ShowMenu.
  - A high occurrence of one-word utterances in your intents. One-word utterances are an exception. Use them sparingly, if at all.
  - Open-ended utterances that can easily apply to other domains or out-of-domain topics.
  - Your corpus doesn't need to repeat the same utterance with different casing or with different word forms that have same lemma. For example, because Trainer Tm can distinguish between manage, manages, and manager, it not only differentiates between "Who does Sam manage?" and "Who manages Sam?", but also understands that these words are related to one another.
    Note
    
    You may be tempted to add misspellings of words. But before you do, use those misspellings in the utterance tester to see if the model recognizes them. You might be surprised at how well it handles them. Also, by not adding misspellings you run less risk of skewing your model in unexpected ways.
Create test cases to ensure the integrity of the intent resolution.
Run the Overview report for skill quality to maintain a balanced training set. Run the anomalies report as well to check for misclassified or unusual utterances.
When you deploy your skill, you can continuously improve the training data by:
- Reviewing the Conversation Logs, summaries of all conversations that have occurred for a specified period. You enable the logging by switching Enable Insights on in Settings.
- Running Skill Quality Reports and by assigning (or reassigning) actual user messages to your intents with the Insights Retrainer. If these reports indicate that unresolvedIntent has a lot of misclassified utterances within the domain intents:
  - Move the in-scope utterances from unresolvedIntent to the domain intents.
  - Move the out-of-scope utterances from the domain intents to unresolvedIntent.

Guidelines for Trainer Ht

Create 12 to 24 sample phrases per intent, if possible. Use unmodified, real-word phrases that include:

vernacular
common misspellings
standard abbreviations that a user might enter ("opty" for "opportunity", for example)
non-standard names, such a product names
spelling variants ("check" and "cheque", for example)

If you don't have any actual data, incorporate these in your own training data. Here are some pointers:

Vary the vocabulary and sentence structure in these starter phrases by one or two permutations using:
- slang words (moolah, lucre, dough)
- standard abbreviations that a user might enter ("opty" for opportunity, for example)
- non-standard names, such a product names
- common expressions (Am I broke? for an intent called AccountBalance)
- alternate wording (Send cash to savings, Send funds to savings, Send money to savings, Transfer cash to savings.)
- different categories of objects (I want to order a pizza, I want to order some food).
- alternate spellings (check, cheque)
- common misspellings ("buisness" for business)
- unusual word order (To checking, $20 send)
Use different concepts to express the same intent, like I am hungry and Make me a pizza
Do not associate a single word or phrase with a specific intent unless that word or phrase indicates the intent. Repeated phrases can skew the intent resolution. For example, starting each OrderPizza utterance with "I want to …" and each ShowMenu intent with "Can you help me to …" may increase the likelihood of the model resolving any user input that begins with "I want to" with OrderPizza and "Can you help me to" with ShowMenu.
Avoid sentence fragments and single words. Instead, use complete sentences (which can be up to 255 characters) that include the action and the entity. If you must use single key word examples, choose them carefully.
Create test cases to ensure the integrity of the test the intent resolution. Because adding a new intent examples can cause regressions, you might end up adding several test phrases to stabilize the intent resolution behavior.

Limits for Training Data Shape and Size

Regarding training data and shape, here are the limits to the number of intents and utterances.

Intents:

Minimum number of intents per skill: 2
Maximum number of intents per skill: 2,500

Utterances:

Maximum number of utterances per skill: 25,000
Minimum number of utterances per intent: 2
Utterance word length: Between 3 and 30 words. Per the guidelines for Trainer Tm, there are exceptions where one or two-word utterances can be appropriate if they are commonly used.

Note

These are technical limits, not recommendations. See Guidelines for Trainer Tm for practical recommendations for shaping your skills and providing robust training data.

Export Intent Data

To log conversations, be sure to enable Enable Insights in Settings > General before you test your intents.

To export data for a skill:

Click to open the side menu and select Development > Skills.
In the tile for the skill, click and select Export Conversations.
Choose Intent Conversation Log, set the logging period, and then click Export.
Review the user input by opening the CSV files in a spreadsheet program.

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