论文标题
命名实体识别的边界平滑
Boundary Smoothing for Named Entity Recognition
论文作者
论文摘要
神经命名实体识别（NER）模型可能很容易遇到过度信心的问题，从而降低了性能和校准。受标签平滑的启发，并受到NER工程边界注释的歧义的驱动，我们提出了边界平滑作为基于跨度神经NER模型的正则化技术。它将实体概率从注释跨度重新分配到周围的跨度。我们的模型以简单但强大的基线为基础，比在八个著名的NER基准上与以前最先进的系统竞争更好或具有竞争力。进一步的经验分析表明，边界平滑有效地缓解过度信心，改善模型校准，并带来更平坦的神经最小值和更平滑的损失景观。
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.
