Heterogeneous Correlation Aware Regularization for Sequential Confidence Calibration.

Abstract

Despite notable advancements across various tasks, deep sequence recognition models are shown to grapple with the dilemma of over-confidence, leading to unreliable predicted confidence, necessitating the need for calibration. Current efforts predominantly focus on classification model calibration, leaving the sequence recognition model calibration analysis underexplored and challenging. In this work, we discover that the primary reason for over-confidence in sequence recognition models stems from the one-hot encoding target sequence training paradigm and identify two distinct manifestations of over-confidence: perception and semantic context over-confidence. To address these challenges, we propose a heterogeneous correlation aware sequence regularization (HCSR) method that adaptively incorporates correlated sequences into training alongside the target sequence as additional supervision to regularize the probability of the target sequence from arbitrarily escalating. Specifically, a correlated sequence mining (CSM) model is designed, capable of efficiently mining heterogeneous correlated sequences, which can be flexibly customized to search for specific types of correlated sequences in demand to facilitate the calibration of corresponding types of over-confidence in the calibrating model, thereby achieving fine-grained calibration. Meanwhile, an adaptive calibration module is introduced to adaptively coordinate the optimization weights between the target sequence and correlated sequences, enabling the co-calibration among different samples. Comprehensive experiments conducted on several widely employed sequence recognition tasks demonstrate that the proposed method outperforms the current competing methods by a substantial margin.