  Neural named entity recognition (NER) models may easily encounter the
over-confidence issue, which degrades the performance and calibration. Inspired
by label smoothing and driven by the ambiguity of boundary annotation in NER
engineering, we propose boundary smoothing as a regularization technique for
span-based neural NER models. It re-assigns entity probabilities from annotated
spans to the surrounding ones. Built on a simple but strong baseline, our model
achieves results better than or competitive with previous state-of-the-art
systems on eight well-known NER benchmarks. Further empirical analysis suggests
that boundary smoothing effectively mitigates over-confidence, improves model
calibration, and brings flatter neural minima and more smoothed loss
landscapes.
