Chatbot Training

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Chatbot training refers to an improvement in language comprehension and an optimisation of the intention recognition behind a user input. For chatbot training, it is important to enrich existing user intentions with further question possibilities(utterances) and thus ensure that the recognition rate(confidence score) increases and the intentions are recognised even better. On the other hand, it is important to check the questions received during operation and to find out whether requests are received for which no intent or intention is stored. In this case, the chatbot plays the Fallback Message and cannot give an answer to the question. To improve this, the chatbot should be trained to create intents for the requests for which there is no content yet.

When do you train the chatbot? 

The chatbot training should already take place during the development phase to ensure from the beginning that the appropriate content is played out in response to the queries. Nevertheless, training is most important during chatbot operation, because this is when "real" users interact with the chatbot and the weaknesses or content gaps usually only become apparent during live operation. During training, it is very easy to see with which content expectations users approach the chatbot and in the event that users expect different/additional content, it is important to intervene quickly and train the chatbot further.

How can I train the chatbot? 

In most cases, this training takes place directly in the NLP services, such as Dialogflow. Here you will find user interactions in a special section, in which you can see how the NLP engine matches intents to user requests. There are 3 possible scenarios:

  1. the NLP service hits the right intentwith the user request
  2. the NLP service assigns a user request to a wrong, existing intent
  3. The NLP service cannot find an intent for the entered request and plays the fallback intent.

Example picture for the training section in Dialogflow:

Training section in Dialogflow


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