We are building our own enterprise chatbot. This chatbot helps enterprise users to run various tasks – invoice processing, inventory review, insurance cases review, order process – it will be compatible with various customer applications. Chatbot is based on TensorFlow Machine learning for user input processing. Machine learning helps to identify user intent, our custom algorithm helps to set conversation context and return response. Context gives control over sequence of conversations under one topic, allowing chatbot to keep meaningful discussion based on user questions/answers. UI part is implemented in two different versions – JET and ADF, to support integration with ADF and JET applications.
Below is the trace of conversations with chatbot:
User statement Ok, I would like to submit payment now sets context transaction. If word payment is entered in the context of transaction, payment processing response is returned. Otherwise if there is no context, word payment doesn’t return any response. Greeting statement – resets context.
Intents are defined in JSON structure. List of intents is defined with patterns and tags. When user types text, TensorFlow Machine learning helps to identify pattern and it returns probabilities for matching tags. Tag with highest probability is selected, or if context was set – tag from context. Response for intent is returned randomly, based on provided list. Intent could be associated with context, this helps to group multiple related intents: Read the complete article here.
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