AVA: A Financial Service Chatbot Based on Deep Bidirectional Transformers

Abstract

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We develop a chatbot using deep bidirectional transformer (BERT) models to handle client questions in financial investment customer service. The bot can recognize 381 intents, decides when to say I don’t know, and escalate escalation/uncertain questions to human operators. Our main novel contribution is the discussion about the uncertainty measure for BERT, where three different approaches are systematically compared with real problems. We investigated two uncertainty metrics, information entropy and variance of dropout sampling, in BERT, followed by mixed-integer programming to optimize decision thresholds. Another novel contribution is the usage of BERT as a language model in automatic spelling correction. Inputs with accidental spelling errors can significantly decrease intent classification performance. The proposed approach combines probabilities from masked language model and word edit distances to find the best corrections for misspelled words. The chatbot and the entire conversational AI system are developed using open-source tools and deployed within our company’s intranet. The proposed approach can be useful for industries seeking similar in-house solutions in their specific business domains. We share all our code and a sample chatbot built on a public data set on GitHub.

Keywords

• chabot
• BERT
• rasa
• bayesian learning
• intent classification