通过不确定性感知的教师学习和学生-学生协作学习提升远程监督命名实体识别的鲁棒性
Improving the Robustness of Distantly-Supervised Named Entity Recognition via Uncertainty-Aware Teacher Learning and Student-Student Collaborative Learning
AI总结：
本文针对远程监督命名实体识别中的标签噪声问题，提出不确定性感知的教师学习和学生-学生协作学习，以减少错误伪标签传播并充分探索误标注样本，在五个数据集上取得优于现有方法的性能。
摘要：
远程监督命名实体识别（DS-NER）在现实场景中被广泛使用。它通过将现有知识库中的实体与文本片段进行匹配，能够有效减轻标注负担，但会受到标签噪声的影响。近期的工作尝试采用教师-学生框架来逐步优化训练标签并提升整体鲁棒性。然而，这些教师-学生方法取得的性能有限，因为教师网络的校准效果不佳会产生错误的伪标签样本，从而导致误差传播。因此，我们提出：（1）不确定性感知的教师学习，利用预测不确定性来减少自训练阶段中错误伪标签的数量；（2）学生-学生协作学习，允许在两个学生网络之间传递可靠标签，而不是不加区分地依赖来自其教师的所有伪标签，并进一步实现对错误标注样本的充分探索，而不是简单地过滤不可靠的伪标签样本。我们在五个DS-NER数据集上评估了我们提出的方法，证明我们的方法优于最先进的DS-NER方法。
英文摘要：
Distantly-Supervised Named Entity Recognition (DS-NER) is widely used in real-world scenarios. It can effectively alleviate the burden of annotation by matching entities in existing knowledge bases with snippets in the text but suffer from the label noise. Recent works attempt to adopt the teacher-student framework to gradually refine the training labels and improve the overall robustness. However, these teacher-student methods achieve limited performance because the poor calibration of the teacher network produces incorrectly pseudo-labeled samples, leading to error propagation. Therefore, we propose: (1) Uncertainty-Aware Teacher Learning that leverages the prediction uncertainty to reduce the number of incorrect pseudo labels in the self-training stage; (2) Student-Student Collaborative Learning that allows the transfer of reliable labels between two student networks instead of indiscriminately relying on all pseudo labels from its teacher, and further enables a full exploration of mislabeled samples rather than simply filtering unreliable pseudo-labeled samples. We evaluate our proposed method on five DS-NER datasets, demonstrating that our method is superior to the state-of-the-art DS-NER methods.
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。
