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Augmenting Customer Support with an NLP-based Receptionist

arXiv 2112.01959 ↗First posted 3 Dec 2021cs.CLcited by 0

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Abstract (arXiv)

In this paper, we show how a Portuguese BERT model can be combined with structured data in order to deploy a chatbot based on a finite state machine to create a conversational AI system that helps a real-estate company to predict its client's contact motivation. The model achieves human level results in a dataset that contains 235 unbalanced labels. Then, we also show its benefits considering the business impact comparing it against classical NLP methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 25 references that are in the map. First posted on arXiv 3 Dec 2021.

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7 of 25 references.

References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021BERTa'u: Ita'u BERT for digital customer servicecited by 11
  3. 2020A Survey on Dialog Management: Recent Advances and Challengescited by 8
  4. 2018COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networkscited by 7
  5. 2020Analyzing HPC Support Tickets: Experience and Recommendationscited by 4
  6. 2018Attention Fusion Networks: Combining Behavior and E-mail Content to Improve Customer Supportcited by 2
  7. 2018Beyond "How may I help you?": Assisting Customer Service Agents with Proactive Responsescited by 2

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