mlsanityData to 7 Oct 2026

Template-Based Named Entity Recognition Using BART

arXiv 2106.01760 ↗First posted 3 Jun 2021cs.CLcited by 153

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Abstract (arXiv)

There is a recent interest in investigating few-shot NER, where the low-resource target domain has different label sets compared with a resource-rich source domain. Existing methods use a similarity-based metric. However, they cannot make full use of knowledge transfer in NER model parameters. To address the issue, we propose a template-based method for NER, treating NER as a language model ranking problem in a sequence-to-sequence framework, where original sentences and statement templates filled by candidate named entity span are regarded as the source sequence and the target sequence, respectively. For inference, the model is required to classify each candidate span based on the corresponding template scores. Our experiments demonstrate that the proposed method achieves 92.55% F1 score on the CoNLL03 (rich-resource task), and significantly better than fine-tuning BERT 10.88%, 15.34%, and 11.73% F1 score on the MIT Movie, the MIT Restaurant, and the ATIS (low-resource task), respectively.

Key facts. Cited by 153 papers in the map (data through 7 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 3 Jun 2021.

Builds on

24 of 35 references.

References · 24

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2019Language Models as Knowledge Bases?cited by 1,922
  6. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  7. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 934
  8. 2016Neural Architectures for Named Entity Recognitioncited by 548
  9. 2016End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRFcited by 341
  10. 2020LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentioncited by 326
  11. 2015Named Entity Recognition with Bidirectional LSTM-CNNscited by 201
  12. 2019Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentencecited by 121
12 more
  1. 2020Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classificationcited by 98
  2. 2020Few-Shot Named Entity Recognition: A Comprehensive Studycited by 41
  3. 2017Fast and Accurate Entity Recognition with Iterated Dilated Convolutionscited by 40
  4. 2019Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networkscited by 33
  5. 2019Hierarchical Contextualized Representation for Named Entity Recognitioncited by 32
  6. 2018Design Challenges and Misconceptions in Neural Sequence Labelingcited by 28
  7. 2019Label-Agnostic Sequence Labeling by Copying Nearest Neighborscited by 27
  8. 2019Hierarchically-Refined Label Attention Network for Sequence Labelingcited by 25
  9. 2019GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labelingcited by 22
  10. 2020Example-Based Named Entity Recognitioncited by 19
  11. 2020Uncertainty-Aware Label Refinement for Sequence Labelingcited by 11
  12. 2019Named Entity Recognition -- Is there a glass ceiling?cited by 10

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Citing papers per year: 2021: 15; 2022: 48; 2023: 48; 2024: 32; 2025: 8; 2026: 2.

153 papers.

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  1. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  2. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
  3. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  4. 2022Unified Structure Generation for Universal Information Extractioncited by 220
  5. 2022Large Language Models are Few-Shot Clinical Information Extractorscited by 206
  6. 2023Large Language Models for Generative Information Extraction: A Surveycited by 183
  7. 2021Reframing Instructional Prompts to GPTk's Languagecited by 176
  8. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  9. 2023Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Promptscited by 161
  10. 2023GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformercited by 84
  11. 2023LLMs4OL: Large Language Models for Ontology Learningcited by 75
  12. 2021Template-free Prompt Tuning for Few-shot NERcited by 73
38 more
  1. 2024LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Modelscited by 67
  2. 2021CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learningcited by 64
  3. 2023Towards Better Chain-of-Thought Prompting Strategies: A Surveycited by 62
  4. 2022RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extractioncited by 60
  5. 2022Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extractioncited by 54
  6. 2023Prompt Engineering for Healthcare: Methodologies and Applicationscited by 52
  7. 2021Paradigm Shift in Natural Language Processingcited by 39
  8. 2022Prompting Language Models for Linguistic Structurecited by 39
  9. 2021Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NERcited by 38
  10. 2022Unleashing the Power of Visual Prompting At the Pixel Levelcited by 37
  11. 2022Dynamic Prefix-Tuning for Generative Template-based Event Extractioncited by 35
  12. 2023PromptNER: Prompting For Named Entity Recognitioncited by 35
  13. 2022InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NERcited by 31
  14. 2023Large Language Model Instruction Following: A Survey of Progresses and Challengescited by 31
  15. 2022AdaPrompt: Adaptive Model Training for Prompt-based NLPcited by 30
  16. 2021LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Promptingcited by 27
  17. 2021An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labelingcited by 26
  18. 2022HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processingcited by 26
  19. 2022Ontology-enhanced Prompt-tuning for Few-shot Learningcited by 24
  20. 2022Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learningcited by 24
  21. 2021NSP-BERT: A Prompt-based Few-Shot Learner Through an Original Pre-training Task--Next Sentence Predictioncited by 23
  22. 2022Decomposed Meta-Learning for Few-Shot Named Entity Recognitioncited by 22
  23. 2022Label Semantics for Few Shot Named Entity Recognitioncited by 21
  24. 2021mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Modelscited by 19
  25. 2023Evaluating the Robustness of Discrete Promptscited by 18
  26. 2023Identifying and Extracting Rare Disease Phenotypes with Large Language Modelscited by 18
  27. 2022DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Populationcited by 17
  28. 2022Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypescited by 17
  29. 2023PromptNER: Prompt Locating and Typing for Named Entity Recognitioncited by 17
  30. 2022How Many Data Samples is an Additional Instruction Worth?cited by 16
  31. 2022Few-shot Named Entity Recognition with Self-describing Networkscited by 16
  32. 2022QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognitioncited by 15
  33. 2022Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognitioncited by 14
  34. 2023Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Surveycited by 14
  35. 2022Prompt-Based Metric Learning for Few-Shot NERcited by 12
  36. 2022Robust and Explainable Identification of Logical Fallacies in Natural Language Argumentscited by 12
  37. 2024Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model promptingcited by 12
  38. 2024LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognitioncited by 11

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