  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.
