In order to understand the usefulness of a decision tree when building a chatbot, you must first understand the main steps involved in creating a chatbot. In concrete terms, what must the administrator/developer do to create a chatbot?
1/ (S)He must select the intents and entities that (s)he wants the machine to recognise, regardless of the tool used (DialogFlow, IBM Watson Assistant, Clustaar, etc.).
2/ The automated chat must then get the system to learn those. To do so, it will input a certain number of sentences and/or words for each pre-identified intent and entity.
Note: this task does not have to be performed for some entities, such as date for instance, since they are natively managed by the system (e.g. for hours, the administrator does not have to input: 00H00, 00H01, 00H02, 00H03, 00H04…, for dates: 01/01/2019, 02/01/2019, 03/01/2019…).
Moreover, the highest quality tools on the market integrate a thesaurus and know the singular/plural forms of words. There is hence no need to, for example, get the system to learn:
- What is a horse? AND “Tell me about horses”,
- Do you sell fruits? AND “Do you have pineapples?”
3/ Finally, the chatbot will set up a decision tree. In concrete terms:
- If intent A + entity A are identified, system responds XXX
- If intent A + entity B are identified, system responds YYY
- If intent B + entity A are identified, system responds ZZZ
- … etc.
The tool can, in the majority of cases, identify the variables missing to be able to formulate a correct answer to the question asked. In which case, it will invite the user to specify them.
Example:
In this article, we have tried to show you how a new generation chatbot (i.e. one that recognises intents and entities in a sentence) works and the main steps involved in its creation. These chatbots do not use machine learning-type processes (the continuous improvement process requires human intervention and is not automatic). The coming years should however see the emergence of a future generation of chatbot creation tools that integrate machine learning.
Our teams remain of course at your disposal to exchange ideas on these subjects.
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