Examples and Exceptions

Check out the following examples to understand the differences between keywords and machine learning matching systems.

Example 1

The bot can enter one of the following interactions.

• Interaction A uses Machine Learning matching system: The section User Says is filled with: Tell me more about your payments policy.
• Interaction B uses Keywords matching system: The section User Says is filled with one word payments.

Now let’s see the bot’s behavior with three different user inputs.

• payments - would trigger Interaction B.
  • Matching score: ML < 70%; Keywords = 100%.
  • Explanation: The matching score for interaction B equals 100% while for interaction A the matching score is less than 70% (below the confidence score).
• tell me about payments policy would trigger interaction A.
  • Matching score: ML => 70% but it’s < 100%; Keywords: 100%
  • Explanation: Even though the matching score for the interaction A is lower, ML has preference and will be executed.
• payments policy: would trigger Interaction B
  • Matching score: ML < 70%, Keywords = 100%;
  • Explanation: The matching score for machine learning is below the required threshold. Interaction A won’t be considered so the bot will go for the Interaction B.

Example 2

We’ve got two following interactions:

• Interaction A - based on ML matching engine. User Says section is filled with sys.any system entity sys.any:any .
• Interaction B - based on keywords matching engine. User Says section is filled with a user entity named Movies. The entity has two values: Star Wars and Punisher.

Now let’s check how is our bot going to behave after the following user input is added:

• something
  • Matching score: ML = 100%, Keywords = 0%; triggered Interaction A
  • Explanation: sys.any Is the only possible entity what won’t have preference while powered by the ML matching system. Interaction B matching score equals 0%. The bot selects Interaction A as this is the only possible variant.
• Punisher
  • Matching score: ML = 100%, Keywords = 100%; triggered Interaction B
  • Explanation: Having in mind that sys.any Is never prioritized, the bot will follow Interaction B which has matching score 100%.

Example 3

Let’s see the last example. We’ve got two interactions:

• Interaction A - based on ML matching system. User Says filled with a phrase payments.
• Interaction B - based on Keywords matching system. User says filled with a phrase payments.

Both interactions have identical User Says field. Let’s check how will this scenario go.

• payments
  • Matching score: ML > 70%, Keywords = 100%; triggers Interaction A
  • Explanation: ML interaction A is prioritized as it’s ML > 70%.
• transfers
  • Matching score: ML = 0%, Keywords = 0%; triggers Fallback Interaction If ON.
  • Explanation: No condition has been met.

Interactions can have vary matching systems. Select the one that is more relevant and gives bigger chances to be selected by the bot.