CUE: An Uncertainty Interpretation Framework for Text Classifiers Built
  on Pre-Trained Language Models
Jiazheng Li Zhaoyue Sun Bin Liang Lin Gui Yulan He
Text classifiers built on Pre-trained Language Models (PLMs) have achieved
remarkable progress in various tasks including sentiment analysis, natural
language inference, and question-answering. However, the occurrence of
uncertain predictions by these classifiers poses a challenge to their
reliability when deployed in practical applications. Much effort has been
devoted to designing various probes in order to understand what PLMs capture.
But few studies have delved into factors influencing PLM-based classifiers'
predictive uncertainty. In this paper, we propose a novel framework, called
CUE, which aims to interpret uncertainties inherent in the predictions of
PLM-based models. In particular, we first map PLM-encoded representations to a
latent space via a variational auto-encoder. We then generate text
representations by perturbing the latent space which causes fluctuation in
predictive uncertainty. By comparing the difference in predictive uncertainty
between the perturbed and the original text representations, we are able to
identify the latent dimensions responsible for uncertainty and subsequently
trace back to the input features that contribute to such uncertainty. Our
extensive experiments on four benchmark datasets encompassing linguistic
acceptability classification, emotion classification, and natural language
inference show the feasibility of our proposed framework. Our source code is
available at: https://github.com/lijiazheng99/CUE.