论文标题
估计谣言验证模型的预测不确定性
Estimating predictive uncertainty for rumour verification models
论文作者
论文摘要
无法正确解决在线流传的谣言可能会带来有害的现实后果。我们提出了一种将模型和数据不确定性估计纳入自然语言处理模型进行自动谣言验证的方法。我们表明，这些估计值可用于滤除可能是错误的模型预测，以便可以通过人类事实检查者优先考虑这些困难实例。我们提出了两种基于不确定性的实例拒绝，监督和无监督的方法。我们还展示了如何使用不确定性估计来将模型性能解释为谣言。
The inability to correctly resolve rumours circulating online can have harmful real-world consequences. We present a method for incorporating model and data uncertainty estimates into natural language processing models for automatic rumour verification. We show that these estimates can be used to filter out model predictions likely to be erroneous, so that these difficult instances can be prioritised by a human fact-checker. We propose two methods for uncertainty-based instance rejection, supervised and unsupervised. We also show how uncertainty estimates can be used to interpret model performance as a rumour unfolds.
