Granularity of Intents

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The granularity of intents represents the level of detail and content of a chatbot's intents. This means that the more granular a chatbot is built, the more individually it can respond to certain requests. This is illustrated in the following section with an example:

  • Fine granular intent:

Question: "Does the Bundestag offer group tours?"

Answer: "Group tours of the Bundestag can also be booked. You can find more information and bookings here:ührungen"

  • Coarse granular intent:

Question: "Is the group tour in the Bundestag barrier-free?"

Answer: "All information about group tours can be found here:ührungen"

How granular should intents be?

The granularity depends very much on the use case and the complexity. It often makes sense at the beginning not to plan the content of a chatbot with too much gran ularity. During the development and especially during the test phase, one should pay attention to the interaction of the user with the chatbot, in order to then still be able to decide how deeply users want to go into the detail. Of course, it is possible to adjust the granularity during and after the introduction of the chatbot and to create new intents to ensure the level of detail.

Which problems can occur with too granular intents?

The more detailed the intents, the more difficult it is to maintain the content of a chatbot. Of course, it helps to name the created intents in a meaningful way to ensure that several developers can work on one chatbot. However, there is another challenge that arises when the intents are set up too granularly. When requests are very similar and very close to each other, it is difficult for the NLP service to identify the correct intent behind a user's request. The NLP service has to decide how sure it is that this request belongs to this intention by creating different question possibilities (utterances). If these utterances are too similar, the confidence score drops and the machine is no longer sure of the intention behind the query.

Overall, it can be said that a middle ground should be chosen, which will crystallise through testing with real users.


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