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.

这篇论文介绍了在自然语言理解（NLU）任务中使用随机重量平均-高斯（SWAG）进行贝叶斯不确定性建模的方法，并将其应用于标准的自然语言推理（NLI）任务中，证明该方法以预测准确度和与人类注释不一致性相关性等方面的有效性。我们认为，SWAG中的不确定性表示更好地反映了主观解释和人类语言理解中存在的自然变化。结果揭示了不确定性建模在NLU任务中的重要性，这是神经语言建模经常忽略的方面。

带有随机权重平均的不确定性感知自然语言推理