  Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates
the data scarcity problem in NER by automatically generating training samples.
Unfortunately, the distant supervision may induce noisy labels, thus
undermining the robustness of the learned models and restricting the practical
application. To relieve this problem, recent works adopt self-training
teacher-student frameworks to gradually refine the training labels and improve
the generalization ability of NER models. However, we argue that the
performance of the current self-training frameworks for DS-NER is severely
underestimated by their plain designs, including both inadequate student
learning and coarse-grained teacher updating. Therefore, in this paper, we make
the first attempt to alleviate these issues by proposing: (1) adaptive teacher
learning comprised of joint training of two teacher-student networks and
considering both consistent and inconsistent predictions between two teachers,
thus promoting comprehensive student learning. (2) fine-grained student
ensemble that updates each fragment of the teacher model with a temporal moving
average of the corresponding fragment of the student, which enhances consistent
predictions on each model fragment against noise. To verify the effectiveness
of our proposed method, we conduct experiments on four DS-NER datasets. The
experimental results demonstrate that our method significantly surpasses
previous SOTA methods.
