# Augmenting Customer Support with an NLP-based Receptionist

arXiv: https://arxiv.org/abs/2112.01959 · HTML: https://mlsanity.com/paper/2112.01959/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 3 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTa'u: Ita'u BERT for digital customer service](https://mlsanity.com/paper/2101.12015/) (2021)
- [A Survey on Dialog Management: Recent Advances and Challenges](https://mlsanity.com/paper/2005.02233/) (2020)
- [COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks](https://mlsanity.com/paper/1807.01337/) (2018)
- [Analyzing HPC Support Tickets: Experience and Recommendations](https://mlsanity.com/paper/2010.04321/) (2020)
- [Attention Fusion Networks: Combining Behavior and E-mail Content to Improve Customer Support](https://mlsanity.com/paper/1811.03169/) (2018)
- [Beyond "How may I help you?": Assisting Customer Service Agents with Proactive Responses](https://mlsanity.com/paper/1811.10686/) (2018)

## 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.