命名实体识别中的数据增强方法是否适用于不确定性估计？
Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?
AI总结：
本文评估数据增强对NER置信度校准与不确定性估计的作用，发现其在域内、跨体裁和跨语言设置下均有效，且低困惑度增强句与更大增强规模效果更好。
摘要：
本工作研究了数据增强对命名实体识别（NER）任务中置信度校准和不确定性估计的影响。为了推动NER在医疗和金融等安全关键领域的未来发展，在将包括预训练语言模型（PLM）在内的深度神经网络（DNN）作为实际应用部署时，获得带有经过校准的置信度的准确预测至关重要。然而，DNN容易出现校准错误，这限制了其适用性。此外，现有的校准和不确定性估计方法计算成本高昂。我们在NER中的研究发现，数据增强在跨体裁和跨语言场景下改善了校准和不确定性，尤其是在域内场景中。此外，我们表明，当数据增强生成句子的困惑度较低时，NER的校准往往更有效；并且增加增强规模会进一步改善校准和不确定性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
