Intents

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Chatbot technologies use machine learning to assign the natural language of the user to a pre-defined intention.

Basic principle of Intents

Common systems such as Google Dialogflow [1], IBM Watson Assistant [2] and Microsoft LUIS [3] usually work according to the Intents principle.

In the process, user intentions and intents relevant to the use case of the chatbot are defined in advance. This is done by storing possible user inputs and a corresponding response. User queries are assigned to one or more of the already defined intents on the basis of the user expression (Intent Matching). The NLP Services usually provide an Confidence Level which states how secure the system is in assigning the user expression to an intent.

Components of an Intent

An intent mainly consists of the following components:

  1. Intentname
  2. utterances
  3. response

Further components may be added depending on the technology used:

  1. context
  2. Events
  3. actions
  4. Parameters / contained entities
  5. fulfillment

Example Intent

Intent:

telephone number

Utterances:

"What's your phone number?"

"Where can I reach you by phone?"

"Give me the phone number, please."

"I'm afraid I couldn't find your number anywhere. Could you please tell me?"

Response:

"Sure! Our phone number is 0931 123456789."

Important Intents

  • Default Fallback Intent
  • Welcome Intent

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Sources

[1] https://dialogflow.com/docs/intents

[2] https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-utterance

[3] https://cloud.ibm.com/docs/services/assistant?topic=assistant-intents