Uncertainty-Aware Continual Learning for Open-World Intent Discovery Under an evolving Label Space

The paper introduces an uncertainty‑aware probabilistic framework for continual discovery of new user intents in open‑world settings where the label space evolves. It encodes utterances with an adaptive β‑VAE, uses classifier confidence, posterior uncertainty, and DP‑GMM likelihood to separate known from novel intents, and promotes only reliable clusters to new labels while employing replay and Elastic Weight Consolidation to avoid forgetting. Experiments demonstrate high novelty precision, stable adaptation across phases, and limited forgetting, with qualitative results showing coherent semantic clusters despite conservative label expansion.

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