Chat con bot caliente
Automating interactions between your company and people is the necessary next step in the digitalization process.
It will reduce costs, drive organizational efficiency, increase employee happiness and customer satisfaction.
So, assuming we extracted all the required feature values from the sample conversations in the required format, we can then train an AI model like LSTM followed by softmax to predict the Referring to the above figure, this is what the ‘dialogue management’ component does. — As mentioned above, we want our model to be context aware and look back into the conversational history to predict the This is akin to a time-series model (pls see my other LSTM-Time series article) and hence can be best captured in the memory state of the LSTM model.
The amount of conversational history we want to look back can be a configurable hyper-parameter to the model.
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If the template requires some placeholder values to be filled up, those values are also passed by the dialogue manager to the generator.
Then the appropriate message is displayed to the user and the bot goes into a wait mode listening for the user input.
Have a bot do it and focus on more difficult cases yourself.
First, lets see what all things do we need to determine an appropriate response at any given moment of the conversational flow?
The intent and the entities together will help to make a corresponding API call to a weather service and retrieve the results, as we will see later.
I will not go into the details of extracting each feature value here.
It can be referred from the documentation of rasa-core link that I provided above.