Intent Detection at Scale: Tuning a Generic Model using Relevant Intents

TL;DR

This paper introduces a scalable intent detection system that combines a generic model with client-specific relevant intents, reducing costs and improving personalization in customer support applications.

Contribution

It presents a novel approach to scale intent detection by integrating relevant intents as features, enhancing adaptability and performance over industry-specific models.

Findings

Significantly improved accuracy over industry-specific models

Resilient performance despite changes in client relevant intents

Cost-effective and easily maintainable system

Abstract

Accurately predicting the intent of customer support requests is vital for efficient support systems, enabling agents to quickly understand messages and prioritize responses accordingly. While different approaches exist for intent detection, maintaining separate client-specific or industry-specific models can be costly and impractical as the client base expands. This work proposes a system to scale intent predictions to various clients effectively, by combining a single generic model with a per-client list of relevant intents. Our approach minimizes training and maintenance costs while providing a personalized experience for clients, allowing for seamless adaptation to changes in their relevant intents. Furthermore, we propose a strategy for using the clients relevant intents as model features that proves to be resilient to changes in the relevant intents of clients -- a common occurrence in production environments. The final system exhibits significantly superior performance compared to industry-specific models, showcasing its flexibility and ability to cater to diverse client needs.