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

TL;DR

This paper presents a real-time commercial system for detecting Purpose of Call statements in English business call transcripts, combining rule-based and neural methods, achieving high accuracy and low latency.

Contribution

It introduces a hybrid transformer-based and rule-based model for Purpose of Call detection, with detailed analysis and deployment insights.

Findings

Achieved 88.6 F1 score on real-world data

Developed a hybrid model combining rules and transformers

Provided insights into language patterns in call transcripts

Abstract

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.