Intent, Entity, and Labelled Data List.docx

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The proposed research intends to improve the current service desk model by using Conversational Language Understanding (CLU) processes embedded in the chatbot model, to understand the user’s input and automate the ticket resolution process as well as improve the customer service experience and efficiency. The CLU data will be trained, thus it will be able to cover all the possible user input. The chatbot will then be designed to have five main dialogue flows consisting of, changing the user’s current password, checking the user’s mobile number that is listed in Azure Active Directory (AAD), updating the user’s mobile number in AAD, creating a new ticket to the ticketing system, and creating a follow-up ticket to the ticketing system. A trained CLU data with a high prediction score based on the proposed dialogue flow will then be embedded with the chatbot design. It would produce a next-level chatbot that is able to understand the user’s intent, classify the user’s intent, automate the user’s Level 1 (L1) proposed request without any human technician’s interaction, and create a ticket in the ticketing system for any request that is not covered by the chatbot yet.