Uncertainty Estimation on Sequential Labeling via Uncertainty
  Transmission
Jianfeng He Linlin Yu Shuo Lei Chang-Tien Lu Feng Chen
Sequential labeling is a task predicting labels for each token in a sequence,
such as Named Entity Recognition (NER). NER tasks aim to extract entities and
predict their labels given a text, which is important in information
extraction. Although previous works have shown great progress in improving NER
performance, uncertainty estimation on NER (UE-NER) is still underexplored but
essential. This work focuses on UE-NER, which aims to estimate uncertainty
scores for the NER predictions. Previous uncertainty estimation models often
overlook two unique characteristics of NER: the connection between entities
(i.e., one entity embedding is learned based on the other ones) and wrong span
cases in the entity extraction subtask. Therefore, we propose a Sequential
Labeling Posterior Network (SLPN) to estimate uncertainty scores for the
extracted entities, considering uncertainty transmitted from other tokens.
Moreover, we have defined an evaluation strategy to address the specificity of
wrong-span cases. Our SLPN has achieved significant improvements on two
datasets, such as a 5.54-point improvement in AUPR on the MIT-Restaurant
dataset.