# Developing a Production System for Purpose of Call Detection in Business Phone Conversations

arXiv: https://arxiv.org/abs/2205.06904 · HTML: https://mlsanity.com/paper/2205.06904/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 13 May 2022.

## Builds on (most cited first, 1 of 1)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)

## Built on by (most cited first, 6 of 6)

- [Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective](https://mlsanity.com/paper/2310.19233/) (2023)
- [Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs](https://mlsanity.com/paper/2311.00681/) (2023)
- [AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching](https://mlsanity.com/paper/2305.17619/) (2023)
- [An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts](https://mlsanity.com/paper/2209.13736/) (2022)
- [LLM-Based Insight Extraction for Contact Center Analytics and Cost-Efficient Deployment](https://mlsanity.com/paper/2503.19090/) (2025)
- [DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations](https://mlsanity.com/paper/2510.08152/) (2025)

## Abstract (arXiv)

For agents at a contact centre receiving calls, the most important piece of information is the reason for a given call. An agent cannot provide support on a call if they do not know why a customer is calling. In this paper we describe our implementation of a commercial system to detect Purpose of Call statements in English business call transcripts in real time. We present a detailed analysis of types of Purpose of Call statements and language patterns related to them, discuss an approach to collect rich training data by bootstrapping from a set of rules to a neural model, and describe a hybrid model which consists of a transformer-based classifier and a set of rules by leveraging insights from the analysis of call transcripts. The model achieved 88.6 F1 on average in various types of business calls when tested on real life data and has low inference time. We reflect on the challenges and design decisions when developing and deploying the system.