Uncertainty Estimation for the Open-Set Text Classification systems

This paper focuses on accurate uncertainty estimation for open-set text classification (OSTC) systems, where text samples can be classified into existing classes or rejected as unknown. It adapts the Holistic Uncertainty Estimation (HolUE) method for the text domain to capture text and gallery uncertainties, and proposes a new OSTC benchmark.