  This paper introduces Bayesian uncertainty modeling using Stochastic Weight
Averaging-Gaussian (SWAG) in Natural Language Understanding (NLU) tasks. We
apply the approach to standard tasks in natural language inference (NLI) and
demonstrate the effectiveness of the method in terms of prediction accuracy and
correlation with human annotation disagreements. We argue that the uncertainty
representations in SWAG better reflect subjective interpretation and the
natural variation that is also present in human language understanding. The
results reveal the importance of uncertainty modeling, an often neglected
aspect of neural language modeling, in NLU tasks.
