![]() Many businesses are excited about the prospect of using closed-domain chatbots to interact directly with their customer base, which comes with many benefits such as cost reduction, zero downtime, or no prejudices. Their goals are to help users accomplish specific tasks, where typical examples range from order placement to customer support therefore, they are also known as task-oriented bots. Unlike open-domain bots, closed-domain chatbots are designed to transform existing processes that rely on human agents. New advances in this field are mostly data-driven and end-to-end systems based on statistical models and neural conversational models aim to achieve human-like conversations through a more scalable and adaptable learning process on free-form and large data sets, such as those given in Ref and. Earlier generations of open-domain bots, such as those mentioned in Ref, relied heavily on hand-crafted rules and recursive symbolic evaluations to capture the key elements of human-like conversation. Open-domain chatbots, also known as chitchat bots, can mimic human conversations to the greatest extent in topics of almost any kind, thus are widely engaged for socialization, entertainment, emotional companionship, and marketing. Since their first appearances decades ago, chatbots have always been marking the apex of artificial intelligence as forefront of all major AI revolutions, such as human–computer interaction, knowledge engineering, expert system, natural language processing, natural language understanding, deep learning, and many others. We share all our code and a sample chatbot built on a public data set on GitHub. The proposed approach can be useful for industries seeking similar in-house solutions in their specific business domains. The chatbot and the entire conversational AI system are developed using open-source tools and deployed within our company’s intranet. The proposed approach combines probabilities from masked language model and word edit distances to find the best corrections for misspelled words. Inputs with accidental spelling errors can significantly decrease intent classification performance. Another novel contribution is the usage of BERT as a language model in automatic spelling correction. We investigated two uncertainty metrics, information entropy and variance of dropout sampling, in BERT, followed by mixed-integer programming to optimize decision thresholds. Our main novel contribution is the discussion about the uncertainty measure for BERT, where three different approaches are systematically compared with real problems. The bot can recognize 381 intents, decides when to say I don’t know, and escalate escalation/uncertain questions to human operators. We develop a chatbot using deep bidirectional transformer (BERT) models to handle client questions in financial investment customer service. The Vanguard Group, Malvern, PA, United States. ![]()
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