Uncertainty approximation in text classification is an important area with applications in domain adaptation and interpretability. The most widely used uncertainty approximation method is Monte Carlo Dropout, which is computationally expensive as it requires multiple forward passes through the model. A cheaper alternative is to simply use a softmax to estimate model uncertainty. However, prior work has indicated that the softmax can generate overconfident uncertainty estimates and can thus be tricked into producing incorrect predictions. In this paper, we perform a thorough empirical analysis of both methods on five datasets with two base neural architectures in order to reveal insight into the trade-offs between the two. We compare the methods' uncertainty approximations and downstream text classification performance, while weighing their performance against their computational complexity as a cost-benefit analysis, by measuring runtime (cost) and the downstream performance (benefit). We find that, while Monte Carlo produces the best uncertainty approximations, using a simple softmax leads to competitive uncertainty estimation for text classification at a much lower computational cost, suggesting that softmax can in fact be a sufficient uncertainty estimate when computational resources are a concern.

翻译：文本分类的不确定性近似性是应用领域适应性和可解释性方面的一个重要领域。最广泛使用的不确定性近似法是蒙特卡洛流出法,计算成本昂贵,因为它要求通过模型进行多重前方传球。一个更便宜的替代办法是简单使用软式算法来估计模型的不确定性。然而,先前的工作表明软式马克思可以产生过于自信的不确定性估计,从而被骗得出不正确的预测。在本文件中,我们对五套有两种基本神经结构的数据集的两种方法进行了彻底的经验分析,以揭示对两者之间权衡的洞察力。我们比较了方法的不确定性近似性和下游文本分类性能,同时将其与计算复杂性作为成本效益分析加以权衡,同时测量运行时间(成本)和下游性能(收益)。我们发现,虽然蒙特卡洛使用简单的软式算法可以以低得多的计算成本对文本分类进行竞争性不确定性估计,但我们发现,在计算资源时软式马克实际上可以算出足够的不确定性估计。