Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging
This work addresses the often-neglected aspect of uncertainty modeling in neural language understanding, which is important for researchers and practitioners in natural language processing.
The paper tackled the problem of modeling uncertainty in natural language inference tasks by applying Stochastic Weight Averaging-Gaussian (SWAG), resulting in improved prediction accuracy and better correlation with human annotation disagreements.
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.