Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates
the burden of annotation, but meanwhile suffers 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, we argue that
these teacher-student methods achieve limited performance because poor network
calibration produces incorrectly pseudo-labeled samples, leading to error
propagation. Therefore, we attempt to mitigate this issue by proposing: (1)
Uncertainty-aware Teacher Learning that leverages the prediction uncertainty to
guide the selection of pseudo-labels, avoiding 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 completely relying on all pseudo-labels from its teacher.
Meanwhile, this approach allows a full exploration of mislabeled samples rather
than simply filtering unreliable pseudo-labeled samples. Extensive experimental
results on five DS-NER datasets demonstrate that our method is superior to
state-of-the-art teacher-student methods.

提出了一种减轻标签噪声的 Distantly-Supervised Named Entity Recognition 方法，其中包括 Uncertainty-aware Teacher Learning 和 Student-student Collaborative Learning 两种技术。实验结果表明，该方法在五个 DS-NER 数据集上优于其他的 teacher-student 方法。