Intent Detection at Scale: Tuning a Generic Model using Relevant Intents
Nichal Narotamo David Aparicio Tiago Mesquita Mariana Almeida
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.