Check out the following examples to understand the differences between keywords and machine learning matching systems.
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
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 policywould 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.
We’ve got two following interactions:
- Interaction A - based on ML matching engine. User Says section is filled with
- Interaction B - based on keywords matching engine. User Says section is filled with a user entity named
Movies. The entity has two values:
Now let’s check how is our bot going to behave after the following user input is added:
- Matching score: ML = 100%, Keywords = 0%; triggered Interaction A
sys.anyIs 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.
- Matching score: ML = 100%, Keywords = 100%; triggered Interaction B
- Explanation: Having in mind that
sys.anyIs never prioritized, the bot will follow Interaction B which has matching score 100%.
Let’s see the last example. We’ve got two interactions:
- Interaction A - based on ML matching system. User Says filled with a phrase
- Interaction B - based on Keywords matching system. User says filled with a phrase
Both interactions have identical User Says field. Let’s check how will this scenario go.
- Matching score: ML > 70%, Keywords = 100%; triggers Interaction A
- Explanation: ML interaction A is prioritized as it’s ML > 70%.
- 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.