Efficient Uncertainty Quantification for Multilabel Text Classification

Abstract

Despite rapid advances of modern artificial intelligence (AI), there is a growing concern regarding its capacity to be explainable, transparent, and accountable. One crucial step towards such AI systems involves reliable and efficient uncertainty quantification methods. Existing approaches to uncertainty quantification in natural language processing (NLP) take a Bayesian Deep Learning approach. However, the latter is known to not be computationally efficient in testing time, thus hindering its applicability in real-life scenarios. This paper proposes a new focus on the efficiency of uncertainty quantification methods, evaluating them on four multi-label text classification tasks. Our novel methods of representing epistemic and aleatoric uncertainties enable efficient uncertainty quantification (around 13 to 45 times faster than existing approaches, depending on architecture) with posterior analysis in the (approximated) latent-and data space. We conduct extensive experiments and studies on diverse neural network architectures (LSTM, CNN and Transformer) to analyse their power. Our results prove the benefits of explicitly modelling uncertainty in neural networks.

Quantifying the Impact of Single-Event Upsets on Neural Networks with Uncertainty Analysis

“…Meanwhile, Alex and Yarin's [16] uncertainty model has achieved state-of-the-art results in semantic segmentation and deep regression. Yu et al [17] proposed a framework for uncertainty quantification that enables efficient a posteriori analysis. Mashrur et al [18] improved the reliability of neural network estimates in small samples by training two shared sub-networks for uncertainty estimation.…”