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.

翻译：本文介绍在自然语言理解任务中运用基于随机权重平均-高斯（SWAG）的贝叶斯不确定性建模方法。我们将该方法应用于自然语言推理的标准任务，并从预测准确率和与人工标注分歧的相关性两方面论证其有效性。我们认为SWAG中的不确定性表示能更准确地反映主观解释以及人类语言理解中固有的自然变异。研究结果揭示了不确定性建模这一神经语言建模中常被忽视的方面，在自然语言理解任务中的重要性。