Federated Learning for Intent Classification
Marten Cureton, Andrea Corradini · 2023
This research focuses on Federated Learning for intent classification as a core element to any conversational system. Since the level of accuracy needed for intent classification is typically quite high and considering the challenges with accessing enough conversational data with which to train and develop a classification models, we exploit Federated Learning to evaluate its feasibility in improving classification accuracy over more traditional, centralized approaches. The research findings are positive, showing an increase in performance across all metrics and achieving a higher accuracy on average across all the tests conducted.