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This paper introduces Bayesian uncertainty modeling using Stochastic WeightAveraging-Gaussian (SWAG) in Natural Language Understanding (NLU) tasks. Weapply the approach to standard tasks in natural language inference (NLI) anddemonstrate the effectiveness of the method in terms of prediction accuracy andcorrelation with human annotation disagreements. We argue that the uncertaintyrepresentations in SWAG better reflect subjective interpretation and thenatural variation that is also present in human language understanding. Theresults reveal the importance of uncertainty modeling, an often neglectedaspect of neural language modeling, in NLU tasks.

Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging