  Named Entity Recognition (NER) is an essential steppingstone in the field of
natural language processing. Although promising performance has been achieved
by various distantly supervised models, we argue that distant supervision
inevitably introduces incomplete and noisy annotations, which may mislead the
model training process. To address this issue, we propose a robust NER model
named BOND-MoE based on Mixture of Experts (MoE). Instead of relying on a
single model for NER prediction, multiple models are trained and ensembled
under the Expectation-Maximization (EM) framework, so that noisy supervision
can be dramatically alleviated. In addition, we introduce a fair assignment
module to balance the document-model assignment process. Extensive experiments
on real-world datasets show that the proposed method achieves state-of-the-art
performance compared with other distantly supervised NER.
