This work investigates the impact of data augmentation on confidence calibration and uncertainty estimation in Named Entity Recognition (NER) tasks. For the future advance of NER in safety-critical fields like healthcare and finance, it is essential to achieve accurate predictions with calibrated confidence when applying Deep Neural Networks (DNNs), including Pre-trained Language Models (PLMs), as a real-world application. However, DNNs are prone to miscalibration, which limits their applicability. Moreover, existing methods for calibration and uncertainty estimation are computational expensive. Our investigation in NER found that data augmentation improves calibration and uncertainty in cross-genre and cross-lingual setting, especially in-domain setting. Furthermore, we showed that the calibration for NER tends to be more effective when the perplexity of the sentences generated by data augmentation is lower, and that increasing the size of the augmentation further improves calibration and uncertainty.

通过数据增强来提高命名实体识别的置信度校准和不确定性估计，在安全关键领域如医疗保健和金融中应用深度神经网络时实现准确的预测非常重要。本研究发现数据增强在跨领域和跨语言的环境中，尤其是在领域内环境中，可以改善命名实体识别的置信度校准和不确定性。此外，研究还表明，当通过数据增强生成的句子的困惑度较低时，命名实体识别的置信度校准更为有效，并且增加增强的规模可以进一步改善置信度校准和不确定性。

数据增强方法对命名实体识别是否适用于不确定性估计？