Uncertainty-Aware Reliable Text Classification

TL;DR

This paper introduces an evidential uncertainty framework for text classification that improves out-of-distribution detection, outperforming existing methods and adaptable to various neural network architectures.

Contribution

It applies evidential uncertainty to NLP, proposing a cost-effective method using auxiliary outliers and pseudo samples for better OOD detection.

Findings

Outperforms existing OOD detection methods

Compatible with RNNs and transformers

Effectively models vacuity and dissonance in uncertainty

Abstract

Deep neural networks have significantly contributed to the success in predictive accuracy for classification tasks. However, they tend to make over-confident predictions in real-world settings, where domain shifting and out-of-distribution (OOD) examples exist. Most research on uncertainty estimation focuses on computer vision because it provides visual validation on uncertainty quality. However, few have been presented in the natural language process domain. Unlike Bayesian methods that indirectly infer uncertainty through weight uncertainties, current evidential uncertainty-based methods explicitly model the uncertainty of class probabilities through subjective opinions. They further consider inherent uncertainty in data with different root causes, vacuity (i.e., uncertainty due to a lack of evidence) and dissonance (i.e., uncertainty due to conflicting evidence). In our paper, we firstly apply evidential uncertainty in OOD detection for text classification tasks. We propose an inexpensive framework that adopts both auxiliary outliers and pseudo off-manifold samples to train the model with prior knowledge of a certain class, which has high vacuity for OOD samples. Extensive empirical experiments demonstrate that our model based on evidential uncertainty outperforms other counterparts for detecting OOD examples. Our approach can be easily deployed to traditional recurrent neural networks and fine-tuned pre-trained transformers.