Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission

• URL: http://arxiv.org/abs/2311.08726v2
• Date: Wed, 5 Jun 2024 15:04:29 GMT
• Title: Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission
• Authors: Jianfeng He, Linlin Yu, Shuo Lei, Chang-Tien Lu, Feng Chen,
• Abstract summary: NER tasks aim to extract entities and predict their labels given a text. This work focuses on UE-NER, which aims to estimate uncertainty scores for the NER predictions. We propose a Sequential Labeling Posterior Network (SLPN) to estimate uncertainty scores for the extracted entities.
• Score: 21.426225910784364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 \url{https://github.com/he159ok/UncSeqLabeling_SLPN}.

Related papers

• A Debiased Nearest Neighbors Framework for Multi-Label Text Classification [13.30576550077694]
  We introduce a DEbiased Nearest Neighbors (DENN) framework for Multi-Label Text Classification (MLTC) To address embedding alignment bias, we propose a debiased contrastive learning strategy, enhancing neighbor consistency on label co-occurrence. For confidence estimation bias, we present a debiased confidence estimation strategy, improving the adaptive combination of predictions from $k$NN and inductive binary classifications.
  arXiv  Detail & Related papers  (2024-08-06T14:00:23Z)
• PAC Prediction Sets Under Label Shift [52.30074177997787]
  Prediction sets capture uncertainty by predicting sets of labels rather than individual labels. We propose a novel algorithm for constructing prediction sets with PAC guarantees in the label shift setting. We evaluate our approach on five datasets.
  arXiv  Detail & Related papers  (2023-10-19T17:57:57Z)
• A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition [74.79785063365289]
  Existing models for named entity recognition (NER) are mainly based on large-scale labeled datasets. We propose a Confidence-based Partial Label Learning (CPLL) method to integrate the prior confidence (given by annotators) and posterior confidences (learned by models) for crowd-annotated NER.
  arXiv  Detail & Related papers  (2023-05-21T15:31:23Z)
• Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition [52.46740830977898]
  We propose a novel seq2seq model named GPRL, which formulates the nested NER task as an entity triplet sequence generation process. Experiments on three nested NER datasets demonstrate that GPRL outperforms previous nested NER models.
  arXiv  Detail & Related papers  (2023-05-12T05:55:34Z)
• Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling [29.744621356187764]
  This paper presents SeqUST, a novel uncertain-aware self-training framework for Neural sequence labeling (NSL) We incorporate Monte Carlo (MC) dropout in Bayesian neural network (BNN) to perform uncertainty estimation at the token level and then select reliable language tokens from unlabeled data. A well-designed masked sequence labeling task with a noise-robust loss supports robust training, which aims to suppress the problem of noisy pseudo labels.
  arXiv  Detail & Related papers  (2023-02-17T02:40:04Z)
• Lifting Weak Supervision To Structured Prediction [12.219011764895853]
  Weak supervision (WS) is a rich set of techniques that produce pseudolabels by aggregating easily obtained but potentially noisy label estimates. We introduce techniques new to weak supervision based on pseudo-Euclidean embeddings and tensor decompositions. Several of our results, which can be viewed as robustness guarantees in structured prediction with noisy labels, may be of independent interest.
  arXiv  Detail & Related papers  (2022-11-24T02:02:58Z)
• Delving into Probabilistic Uncertainty for Unsupervised Domain Adaptive Person Re-Identification [54.174146346387204]
  We propose an approach named probabilistic uncertainty guided progressive label refinery (P$2$LR) for domain adaptive person re-identification. A quantitative criterion is established to measure the uncertainty of pseudo labels and facilitate the network training. Our method outperforms the baseline by 6.5% mAP on the Duke2Market task, while surpassing the state-of-the-art method by 2.5% mAP on the Market2MSMT task.
  arXiv  Detail & Related papers  (2021-12-28T07:40:12Z)
• A Sequence-to-Set Network for Nested Named Entity Recognition [38.05786148160635]
  We propose a novel sequence-to-set neural network for nested NER. We use a non-autoregressive decoder to predict the final set of entities in one pass. Experimental results show that our proposed model achieves state-of-the-art on three nested NER corpora.
  arXiv  Detail & Related papers  (2021-05-19T03:10:04Z)
• Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification [137.9939571408506]
  We estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-16T04:09:04Z)
• BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision [49.42215511723874]
  We propose a new computational framework -- BOND -- to improve the prediction performance of NER models. Specifically, we propose a two-stage training algorithm: In the first stage, we adapt the pre-trained language model to the NER tasks using the distant labels. In the second stage, we drop the distant labels, and propose a self-training approach to further improve the model performance.
  arXiv  Detail & Related papers  (2020-06-28T04:55:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.