  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.
