MProto: Multi-Prototype Network with Denoised Optimal Transport for
  Distantly Supervised Named Entity Recognition
Shuhui Wu Yongliang Shen Zeqi Tan Wenqi Ren Jietian Guo Shiliang Pu Weiming Lu
Distantly supervised named entity recognition (DS-NER) aims to locate entity
mentions and classify their types with only knowledge bases or gazetteers and
unlabeled corpus. However, distant annotations are noisy and degrade the
performance of NER models. In this paper, we propose a noise-robust prototype
network named MProto for the DS-NER task. Different from previous
prototype-based NER methods, MProto represents each entity type with multiple
prototypes to characterize the intra-class variance among entity
representations. To optimize the classifier, each token should be assigned an
appropriate ground-truth prototype and we consider such token-prototype
assignment as an optimal transport (OT) problem. Furthermore, to mitigate the
noise from incomplete labeling, we propose a novel denoised optimal transport
(DOT) algorithm. Specifically, we utilize the assignment result between Other
class tokens and all prototypes to distinguish unlabeled entity tokens from
true negatives. Experiments on several DS-NER benchmarks demonstrate that our
MProto achieves state-of-the-art performance. The source code is now available
on Github.