mlsanityData to 14 Oct 2026

Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission

arXiv 2311.08726 ↗First posted 15 Nov 2023cs.CLcited by 10

Not on the map. Why?

Abstract (arXiv)

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 three datasets, such as a 5.54-point improvement in AUPR on the MIT-Restaurant dataset. Our code is available at urlhttps://github.com/he159ok/UncSeqLabelingSLPN.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 24 of its 43 references that are in the map. First posted on arXiv 15 Nov 2023.

Builds on

24 of 43 references.

References · 24

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  4. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  5. 2015Variational Inference with Normalizing Flowscited by 2,170
  6. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 732
  7. 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 560
  8. 2018Predictive Uncertainty Estimation via Prior Networkscited by 557
  9. 2016Neural Architectures for Named Entity Recognitioncited by 550
  10. 2015Bidirectional LSTM-CRF Models for Sequence Taggingcited by 546
  11. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  12. 2019Deep Evidential Regressioncited by 355
12 more
  1. 2015Named Entity Recognition with Bidirectional LSTM-CNNscited by 201
  2. 2021Deep Deterministic Uncertainty: A Simple Baselinecited by 200
  3. 2019A Unified MRC Framework for Named Entity Recognitioncited by 199
  4. 2020Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Countscited by 164
  5. 2019Practical Deep Learning with Bayesian Principlescited by 158
  6. 2020Uncertainty Aware Semi-Supervised Learning on Graph Datacited by 95
  7. 2021Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classificationcited by 80
  8. 2017Adversarial Dropout for Supervised and Semi-supervised Learningcited by 39
  9. 2021Uncertainty-Aware Reliable Text Classificationcited by 23
  10. 2019Mitigating Uncertainty in Document Classificationcited by 22
  11. 2023Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Trainingcited by 5
  12. 2023Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularizationcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 3; 2026: 1.

10 papers.

  1. 2024A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directionscited by 121
  2. 2024A Survey of Uncertainty Estimation in LLMs: Theory Meets Practicecited by 55
  3. 2025A Head to Predict and a Head to Question: Pre-trained Uncertainty Quantification Heads for Hallucination Detection in LLM Outputscited by 21
  4. 2025Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Modelscited by 18
  5. 2025Efficient Hallucination Detection for LLMs Using Uncertainty-Aware Attention Headscited by 16
  6. 2024Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Modelscited by 9
  7. 2024Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selectioncited by 5
  8. 2024Predictive Uncertainty Quantification for Bird's Eye View Segmentation: A Benchmark and Novel Loss Functioncited by 2
  9. 2024Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Taskscited by 1
  10. 2026Uncertainty Quantification for Named Entity Recognition via Full-Sequence and Subsequence Conformal Predictioncited by 0