Intents

EARLY ACCESS

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When end users contact a chatbot, they have a specific goal. Example: They may contact the chatbot to obtain information, perform an action, or resolve an issue. The chatbot needs to identify the goal of the end user's message so that it can take the relevant action.

Chatbots use intents to identify these goals. The process is as follows:

1. Identify a list of potential goals that end users might have.
2. For each goal, create an intent.
3. Assign the intent to the relevant dialog.
4. When an end user sends a message, the chatbot identifies the relevant intent of the message, and directs the end user to the dialog that is associated with the intent.

Example: For a chatbot that manages bookings:

End user's message	Goal of the message	Intent that is matched for the goal	Dialog to which the conversation is routed
I want to create a new reservation	To create a booking	Create_booking	Create
Need to reschedule my booking	To manage a booking	Manage_booking	Manage
Can I cancel my appointment?	To cancel a booking	Cancel_booking	Cancel

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Training dataset for intents

Each intent has a training dataset that helps the chatbot identify the correct intent. The training dataset contains training phrases and important words.

Training phrases

Training phrases are a set of sample messages that end users are likely to send to the chatbot. These phrases help the chatbot understand what the end user wants. The chatbot can then take the relevant action.

Example:

	Manage_booking intent	Baggage intent
Training phrases	I want to change my booking. Help me manage my booking. I want to reschedule my appointment. Need help to manage my booking. How do I modify my booking?	I want to know about my baggage allowance. How many pieces of baggage can I carry? What is the difference between hand baggage and check in baggage? How much cabin baggage can I carry? What is the check in allowance?

Training phrases can be single words, phrases, or complete sentences. The more the number of training phrases, the higher the probability for the chatbot to take the right action.

Important words

Similar to training phrases, important words also help identify intents. An important word is a single word that helps the chatbot uniquely identify the intent.

Adding important words is optional. But if there are similar intents, adding important words helps identify the correct intent.

In the following example, the important words Manage and Cancel help differentiate between the two intents.

	Manage_booking intent	Cancel_booking intent
Training phrases	I want to change my booking. Help me manage my booking. I want to reschedule my appointment. Need help to manage my booking. How do I modify my booking?	I want to cancel my booking. Help me scrap my booking. I want to call off my appointment.
Important word	Manage	Cancel

Synonyms: When end users respond to your chatbot, they may use variations of the important words. Example: Instead of using the word 'manage', the end user may say 'reschedule'. You need to add these variations to the training dataset. Instead of adding these variations as separate important words, you can add them as synonyms to an existing important word. Example: For the important word 'manage', you can add the following synonyms: reschedule, move, change, and update.

The chatbot takes the same action whether the end user's response contains the important word or a synonym.

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Named entity recognition attributes

Use Named entity recognition (opens in a new tab) (NER) attributes to extract information when end users respond with unstructured text. NER is a part of natural language processing (NLP). The goal of NER is to recognize and categorize information (entities) in the end user's message. These entities can be either a single word or a set of words.

Use NER attributes in combination with intents in chatbots.

NER attributes have the following fields.

Name

The name of the NER attribute. Example: mobile_attribute.

Type

NER attributes can have one of the following data types:

• Standard NER attribute type
• Custom data type

Standard NER attribute types

NER attributes can have one of the following NER-enabled data types:

• City: Data set from GeoNames (opens in a new tab)
• Country: Data set from GeoNames (opens in a new tab)
• Currency: Numeric amounts and currency names, codes, or symbols
• Date: The date is stored in the format YYYY-MM-DD.
  • If an end user's message contains the date in Month/Day/Year format, the date is stored incorrectly.
  • If an end user's message contains the date in a different format, the chatbot converts the date to the format YYYY-MM-DD.
  • The attribute supports only names of English months.
  • If a part of a date is missing, the chatbot stores the value as 0. Example: If an end user's message contains '1st March' and the year is missing, the date is stored as 0-3-1.
  • The attribute accepts the following values: Day: Either from 1 to 31 or is missing Month: Either from 1 to 12 or is missing Year: Either from 1900 to 2100 or is missing
  • If the day (DD) information is present, the month information (MM) must also be present.
  • Example:

    End user's message	Date attribute
    1st of March, 2023	2023-3-1
    01/03/2023	2023-3-1
    1.3.2023	2023-3-1
    1/3/2023	2023-3-1
    1-3-2023	2023-3-1
    First of March, 2023	2023-3-1
    First of March	0-3-1
    March	0-3-0

• Day of week: Text for the days of the week, in English
• Number: Numbers written in digits. Example: Use a NER attribute of type Number to extract the customer number.
• Relative date: Supports the following relative dates.
  • TODAY
  • YESTERDAY
  • TOMORROW
  • DAY_BEFORE_YESTERDAY
  • DAY_AFTER_TOMORROW
  • LAST_MONTH
  • LAST_YEAR
  • THIS_MONTH
  • THIS_YEAR
  • NEXT_MONTH
  • NEXT_YEAR Example:

    End user's message	Relative Date attribute
    That happened today?	TODAY
    Is that the day before yesterday?	DAY_BEFORE_YESTERDAY
    Is that the day after tomorrow	DAY_AFTER_TOMORROW
    Was that the previous month?	LAST_MONTH
    Was that the last month?	LAST_MONTH

Validation for standard data types

Standard data type	Condition	Validation
City	is equal to | is not equal to | is null | is not null	Data set from GeoNames
Country	is equal to | is not equal to | is null | is not null	Data set from GeoNames
Currency	is equal to | is not equal to | is null | is not null	Numeric amounts and currency names/codes/symbols
Date	is equal to | is not equal to | is before | is before or equal to | is after | is after or equal to | is null | is not null	Checks if a word in the text is a date element (day, year, or month)
Day of the week	is equal to | is not equal to | is null | is not null	Textual input for days of the week in English
Number	is equal to | is not equal to | is before | is before or equal to | is after | is after or equal to | is null | is not null	Numbers written with digits

Custom data types

In addition to the NER-enabled standard data types, you can create custom data types for NER attributes. Use custom data types to identify predefined entity values in end user messages.

Create a custom data type and add custom values to it. If the end user's message contains one of the custom values, configure the chatbot to direct the flow accordingly. For an example, see the Use entities to branch a dialog section.

Custom data types are case sensitive. Add values in the same format that you expect end users to use.

All AI-supported languages support custom data types.

Supported data type by language

The availability of an attribute type depends on the chatbot language.

English, Spanish, and Portuguese support the following NER attribute types:

• City
• Country
• Currency
• Date
• Day of week
• Number
• Relative date
• Custom data types

Other languages support the following NER attribute types:

• Number
• Custom data types

Subtype

A subcategory for the NER attribute type. Subtypes enable you to capture 'to' and 'from' values in responses from end users. Example: If your chatbot is for booking flights, use two City type NER attributes with Source and Target subtypes to capture the 'from' and 'to' cities.

NER attributes can have one of the following subtypes:

• Source
• Target
• None

Subtype is optional. But, if you want to assign two NER attributes of the same type to an intent, you must define the subtype for each attribute. Also, the subtype for each of these attributes must be different.

If you do not add the subtype, you can add only one attribute of a specific type to an intent.

Example: For Intent_1, you can add the following attributes of the type City and define their subtypes:

• Location_from attribute - Source subtype
• Location_to attribute - Target subtype
• Location attribute - None subtype

When the end user sends a message, the attributes are recognized as follows:

End user message	Location_from attribute	Location_to attribute	Location attribute
I want to travel from London to Zagreb	London	Zagreb	empty
I want to travel to Zagreb	empty	Zagreb	empty
I want to visit Zagreb	empty	empty	Zagreb

Scope

The scope can be one of the following:

• Dialog: The attribute value is specific to a dialog. In other dialogs, the attribute value either has the default value or is blank.

  Example: The default value of the opt_in attribute is False. In a specific dialog, the opt_in attribute value changes to True. But in all other dialogs, the attribute still has the default value, False.

• Chatbot: The attribute value is the same across all dialogs in the chatbot.

  Example: The default value of the opt_in attribute is False. In a specific dialog, the opt_in attribute value changes to True. In all other dialogs, the attribute now has the value, True.

List

You can select whether the NER attribute is for a single value or a list. If you define the attribute as a single value, the attribute stores the first relevant value from the end user's message. If you define the attribute as a list, the attribute stores all relevant values from the end user's message.

Example: You want to store the name of the city in the city attribute. The end user message is "I want to visit London, Chennai, and Zagreb". If you define the city attribute as a single value, it stores the first city in the end user's response, that is, London. If you define the city attribute as a list, it stores all the cities in the end user's response, that is, London, Chennai, and Zagreb.

To define the attribute as a list, select List. Otherwise, the attribute is set as a single value.

If you define the attribute as a list, you can use other chatbot elements to obtain specific information.

Example: You want to obtain the name of the city from the end user. The end user's message is "I want to visit London, Chennai, and Zagreb".

1. Define the city attribute as a list. city stores all the cities in the end user's response. So, city contains London, Chennai, and Zagreb.
2. Use the Conditions element to identify the number of values in the city attribute.
   1. If the number of values is 1, use this value for the next steps of the chatbot flow.
   2. If the number of values is more than 1, use either the Quick Reply or the Carousel element to ask the end user to provide the specific value from the list of values. In this example, the number of values is 3. So, use a Quick Reply or Carousel element to ask the end user the specific city from the 3 values. "In which of these cities you would like to start your trip? London, Chennai, Zagreb"
3. The end user chooses Zagreb.
4. Use the Save user response element validation to make sure that only 1 value, that is, Zagreb, is now stored in the city attribute.

Available to agents

If you select this field, the NER attribute value is visible to an agent when the chat is transferred from the chatbot to an agent in Inbox.

In Inbox, the attribute value is displayed in the context card of the conversation and the conversation history.

If you also select Sensitive data, the attribute value is displayed as a hashed value in the chatbot editor and Inbox.

To hide the attribute value from agents, deselect this field.

Sensitive data

If you select this field, the attribute value, including default value, is encrypted when it is stored in the database. So, the attribute value is displayed as hashed data in the chatbot editor and Inbox.

Example: The message is displayed as follows. When this field is disabled: Your account balance is 2123.32. When this field is enabled: Your account balance is ####.##.

Use this field if the NER attribute contains sensitive data. This setting is especially useful when the chatbot transfers chats to live agents.

This feature is applicable only for outbound attributes.

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How to

Work with intents

To use intents in your chatbot, do the following.

1. (Optional) By default, the chatbot uses the Chatbots NLU intent engine. Select an intent engine if required.
2. Identify a list of potential goals that end users might have.
3. For each goal, create an intent.
4. Create training phrases for each intent.
5. (Optional) Add important words to the intents to improve identification of the correct intent.
6. (Optional) Use NER attributes in your intents, if you expect end users to respond with unstructured text.
7. Assign the intent to the relevant dialog.

When an end user sends a message, the chatbot identifies the relevant intent of the message, and directs the end user to the dialog that is associated with the intent.

Select an intent engine

Select the intent engine that the chatbot needs to use. You can select one of the following.

• Chatbots NLU: The Infobip intent detection engine.
• Infobip GenAI intent detection: The Infobip Generative AI intent engine. Powered by the language capabilities of GenAI and supports most languages.

If you do not select an intent engine, the chatbot uses the Chatbots NLU engine by default.

Create an intent

You can create an intent either from the Intents tab or from a dialog.

Create intent from the Intents tab

1. On the Infobip web interface (opens in a new tab), go to Automation > Chatbots and go to your chatbot.

2. Go to the Intents tab.

3. (Optional) By default, the chatbot uses the Chatbots NLU intent engine. Select an intent engine if required.

4. Select Add intent.

5. Select the pencil icon or select within the New intent text, and enter the name of the intent.

6. If you selected Infobip GenAI intent detection as the intent engine, enter a description for the intent. The description helps the intent engine understand the purpose of the intent and to identify the intent of the end user's message.

   Example: This intent is used to check the account balance in the chatbot that offers customer support for banking services.

7. Add training phrases to the intent.

Create intent from within a dialog

You can create intents only from standard dialogs. You cannot create intents from the Default, Authentication, and Session Expire dialogs.

Follow these steps.

1. On the Infobip web interface (opens in a new tab), go to Automation > Chatbots and go to your chatbot.

2. In the Dialogs tab, select the dialog for which you want to create the intent.

3. In the Intent Settings section, select the menu and select Create new intent.

4. Enter the name of the intent and select the checkmark.

5. Go to the Intents tab.

6. (Optional) By default, the chatbot uses the Chatbots NLU intent engine. Select an intent engine if required.

7. Select the intent that you created.

8. If you selected Infobip GenAI intent detection as the intent engine, enter a description for the intent. The description helps the intent engine understand the purpose of the intent and to identify the intent of the end user's message.

   Example: This intent is used to check the account balance in the chatbot that offers customer support for banking services.

9. Add training phrases to the intent.

Add training phrases to an intent

You can add training phrases in the following ways.

• Manually
• Generate automatically using AI

Add manually

1. Go to the Intents tab.
2. Select the intent for which you want to add training phrases.
3. In the Training phrases tab, type the training phrase and select Add.

Generate automatically using AI

You can generate a maximum of 60 training phrases at a time. If you want more than 60 training phrases, generate the phrases multiple times, for a maximum of 100 times a month.

If you generate training phrases more than once, the chatbot might create duplicates. It discards these duplicates and adds only unique values to the intent.

Follow these steps to generate a set of training phrases.

1. Go to the Intents tab.

2. Select the intent for which you want to add training phrases.

3. In the Training phrases tab, select Generate using AI.

4. In the Generate phrases side panel > Intent description section, add a description for the intent. You can add a maximum of 512 characters, including spaces, new line, and special characters.

5. In the Example phrases section, add sample messages that you expect the end user to send the chatbot for this intent. These examples guide the AI engine in creating the type of training phrases that you want. Follow these guidelines:

   • You must add a minimum of 2 training phrases and can add a maximum of 10 training phrases.
   • Make sure that the sample training phrases are unique and diverse.

6. In the Intent language field, select the language in which you want the training phrases to be generated.

7. Specify the number of training phrases to generate. You can specify a maximum of 60.

8. Select Generate phrases.

   When the training phrases are ready, the chatbot shows a notification. The training phrases are now available for the intent.

Add an important word to an intent

1. Go to the Intents tab.
2. Select the intent for which you want to add important words.
3. In the Important Words tab, select Add important word.
4. Enter the important word.
5. (Optional) Add synonyms.

Create a NER attribute and add to an intent

Complete the steps in each of the following sections in order.

1. Use an existing intent or create a new intent.
2. Create a custom data type.
3. Create a NER attribute that uses the custom data type.
4. Add the NER attribute to the intent.

Create a custom data type

1. On the Infobip web interface (opens in a new tab), go to Automation > Chatbots and go to your chatbot.

2. Go to the Attributes tab > Custom data types tab.

3. Select Add data type.

4. In the New custom data type window > Name field, enter the name of the custom data type. Example: mobile_devices.

5. Add each value that this data type can contain. Example: mobile phone, tablet, and laptop. Enter the values in the exact format, including upper and lower case, in which you expect the end user to respond.

   To add a custom value, select Add and enter the value.

6. Select Save.

Create a NER attribute

1. Go to the Attributes tab > NER attributes tab.
2. Select Add NER attribute.
3. Configure the following fields. For information about these fields, see the NER attributes section.
   • Name: Enter a name for the attribute. Example: mobile_attribute.
   • Type: Choose the custom data type that you created.
   • Subtype: Select the relevant subtype.
   • Scope: Choose whether the attribute is Dialog or Chatbot.
   • List: Select whether the NER attribute is for a single value or a list.
   • Available to agents: Deselect this field to hide attribute values from agents when the chat is transferred from the chatbot to an agent in Inbox.
   • Sensitive data: Select this field if you want to display attribute values as hashed values in the chatbot editor and Inbox.

Add the NER attribute to the intent

1. Go to the Intents tab.
2. Select the required intent and go to the NER attributes tab.
3. Select Add NER attribute.
4. Choose the NER attribute that you created.

Assign an intent to a dialog

After you create an intent, assign it to the relevant dialog. Example: Assign the Manage_booking intent to the Manage dialog.

You can assign intents only to standard dialogs. You cannot assign intents to the Default, Authentication, and Session Expire dialogs.

1. In the chatbot editor, select the name of the dialog to view the settings.
2. To use an existing intent, choose the intent from the list.
3. To create a new intent for the dialog, see the Create an intent section.

Use entities to branch a dialog

If end users contact your chatbot for similar products or categories, create a separate intent and dialog for each product. When directing the flow, add a branch for each product. Each intent requires a similar set of training phrases.

Also, if there are several products, this leads to a large number of intents and branches.

To avoid this situation, you can do one of the following:

• If there is a small number of similar products, you can branch based on entity values.
• If there is a large number of similar products, you can branch based on entity types.

Branch based on entity values

Example: End users contact your chatbot about different car brands - CarA, CarB, and CarC.

To branch based on entity values, do the following:

1. Create a custom data type for cars and add the brands as attribute values.
2. Create a NER attribute. In the Type field, choose the custom data type.
3. Create an intent that contains training phrases for all car brands.
4. Add the NER attribute to the intent.
5. Branch the chatbot based on the value of the NER attribute.
6. Test the flow in the simulator. Example: If you enter a question about CarB, the chatbot takes you to the dialog for CarB.

Branch based on entity types

Example: End users contact your chatbot about different car brands and models.

• CarA - Model_A1, Model_A2, Model_A3, Model_A4
• CarB - Model_B1, Model_B2, Model_B3, Model_B4
• CarC - Model_C1, Model_C2, Model_C3

To branch based on entity types, do the following:

1. Create a custom data type for each car brand and add the models as attribute values.
   • Entity_CarA = {Model_A1, Model_A2, Model_A3, Model_A4}
   • Entity_CarB = {Model_B1, Model_B2, Model_B3, Model_B4}
   • Entity_CarC = {Model_C1, Model_C2, Model_C3}
2. Create NER attributes for each car brand. In the Type field, choose the custom data types.
3. Create an intent that contains training phrases for all car brands.
4. Add the NER attributes to the intent.
5. Branch the chatbot based on the NER attribute.
6. Test the flow in the simulator. Example: If you enter a question about Model_B3, the chatbot takes you to the dialog for CarB.

Import intents

Automate the process of adding intents and training phrases, instead of manually adding them one by one.

Complete the following steps in order:

1. (Optional) Download a sample file for reference.
2. Create a file that contains intents, training phrases, important words, and synonyms.
3. Upload the completed file.

Download a sample file

1. On the Infobip web interface (opens in a new tab), go to Automation > Chatbots and go to your chatbot.
2. Go to the Intents tab.
3. Select Import.
4. Select Download example.

Create the file

Prepare a file that contains phrases and sentences that end users are likely to use in conversations with your chatbot. Use phrases from your customer-facing portals. Example: online chats with your customer service.

Make sure that the file follows these criteria:

• The file type must be .csv or .txt.
• There must be no empty rows.
• The file must not have a header.
• Training phrases:
  • Add each training phrase to a separate line.
  • Use a semicolon to separate training phrases and intents.
  • Organize the training phrases by intent.
  • Format: Training_Phrase;Intent
• Important words:
  • Add each important word to a separate line.
  • Use a semicolon to separate important words, intents, and the synonyms of the important words.
  • Use commas to separate multiple synonyms.
  • Organize the important words by intent.
  • Format: Important_Word;Intent;Synonym1,Synonym2,Synonym3

Example: For the Welcome and Goodbye intents, the file is as follows: How are you?;Welcome How can I help?;Welcome Thank you.;Goodbye Glad to have been of help.;Goodbye Bye.;Goodbye;farewell,ciao Close;Goodbye;end,finished

Import the file

1. On the Infobip web interface (opens in a new tab), go to Automation > Chatbots and go to your chatbot.
2. Go to the Intents tab.
3. Select Import.
4. Select the file.
5. Select Import.

If there are issues with the file, the chatbot shows an error for incorrect file type or incorrect formatting (for example, wrong delimiter).

Export intents

1. On the Infobip web interface (opens in a new tab), go to Automation > Chatbots and go to your chatbot.
2. Go to the Intents tab.
3. Select one or more intents on the page. To export all intents, select the checkbox next to Intent name.
4. Select Export.

The intents are downloaded in a predefined format as a .csv file.

Rename an intent

1. Select an intent.
2. Either select the name of the intent or select the pencil icon.
3. Enter the new name.

Delete an intent

When you delete an intent, you can no longer add it to a dialog. If an intent is already added to a dialog, the intent is removed from the dialog.

To delete intents, go to the Intents tab and follow one of these options.

Delete a single intent

To delete a single intent, select the Delete icon next to the intent. In the confirmation screen, select Delete.

Delete multiple intents

Select one or more intents on the page and select Delete. To delete all intents, select the checkbox next to the Name column header, and select Delete.

In the confirmation screen, select Delete.

Delete from within an intent

1. Select an intent to open it.
2. Click the Delete icon.
3. In the confirmation screen, select Delete.

Delete a training phrase

To delete training phrases, go to the Intents tab. In the intent for which you want to delete training phrases, go to the Training phrases tab.

Do one of the following.

• To delete a single training phrase, select the Delete icon next to the training phrase.
• To delete multiple training phrases, select one or more training phrases on the page and select Delete.
• To delete all training phrases, select the checkbox next to the Phrases column header, and select Delete.

In the confirmation screen, select Delete.

Delete an important word

To delete important words, go to the Intents tab. In the intent for which you want to delete important words, go to the Important words tab.

Do one of the following.

• To delete a single important word, select the Delete icon next to the important word.
• To delete multiple important words, select one or more important words on the page and select Delete.
• To delete all important words, select the checkbox next to the Word column header, and select Delete.

In the confirmation screen, select Delete.

Delete a NER attribute

To delete NER attributes, go to the Attributes tab > NER attributes tab.

Do one of the following.

• To delete a single NER attribute, select the Delete icon next to the NER attribute.
• To delete multiple NER attributes, select one or more NER attributes on the page and select Delete.
• To delete all NER attributes, select the checkbox next to the Name column header, and select Delete.

In the confirmation screen, select Delete.

Delete a custom data type

To delete custom data types, go to the Attributes tab > Custom data types tab.

Do one of the following.

• To delete a single custom data type, select the Delete icon next to the custom data type.
• To delete multiple custom data types, select one or more custom data types on the page and select Delete.
• To delete all custom data types, select the checkbox next to the Custom type name column header, and select Delete.

In the confirmation screen, select Delete.

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Test intents

Check how well the chatbot recognizes intents.

In the simulator, do the following:

1. Select Start Simulation.
2. Enter an end user message.
3. Select Intent engine.

In the results pane, you can view the following:

Intent matched: The intent with the highest score that was recognized by evaluating the tokenized text against training phrases. If an incorrect intent is matched, follow the steps in the Incorrect intent is matched section.

Route to dialog: The dialog that was linked to the recognized intent when the chatbot was built.

Original text: The original end user message without any changes, such as autocorrection or other post processing actions.

Tokenized text: The text that was obtained after word autocorrection and removing stop words.

Autocorrected: Indicates whether autocorrection was applied. If autocorrection was applied, the field value is true.

Resolved attributes: Resolved NER attributes that are configured for the recognized intent.

For any dialog, the intent engine details are available only outside that dialog.

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Troubleshooting

Number of training phrases in intent higher or lower than others

The number of training phrases for one intent is significantly higher or lower than for others. Use a similar number of training phrases for all intents.

Incorrect intent is matched

If the intent match is not correct, check the Tokenized text section to understand which words were used to evaluate the intent score.

Example: Your chatbot uses the following intents - 'book appointment', 'cancel appointment', and 'reschedule appointment'. If the chatbot incorrectly recognizes 'book appointment' as the intent instead of 'cancel appointment', do one or more of the following:

• Add more training phrases to the correct intent (cancel appointment).
• Relocate some of the training phrases between intents (from 'book appointment' to 'cancel appointment').
• If intents are similar, merge them into a single intent, and use a different approach to distinguish between the actions. Example: Use a combination of named entity recognition (NER) and a custom data type. See Use entities to branch a dialog > Branch based on entity types.

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Analytics

Open the Dialogs and intents dashboard in chatbot analytics to find user messages where the chatbot couldn’t determine the intent.

• If the list includes user start messages, add them as training phrases to your starting intent.
• If it includes unexpected messages, either add them as training phrases or create new intents to handle them.

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