mlsanityData to 7 Oct 2026

FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition

arXiv 2208.11464 ↗First posted 24 Aug 2022cs.CLcited by 7

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Abstract (arXiv)

Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years. Existing approaches for few-shot NER are evaluated mainly under in-domain settings. In contrast, little is known about how these inherently faithful models perform in cross-domain NER using a few labeled in-domain examples. This paper proposes a two-step rationale-centric data augmentation method to improve the model's generalization ability. Results on several datasets show that our model-agnostic method significantly improves the performance of cross-domain NER tasks compared to previous state-of-the-art methods, including the data augmentation and prompt-tuning methods. Our codes are available at https://github.com/lifan-yuan/FactMix.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 35 of its 48 references that are in the map. First posted on arXiv 24 Aug 2022.

Builds on

35 of 48 references.

References · 35

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23 more
  1. 2020CrossNER: Evaluating Cross-Domain Named Entity Recognitioncited by 102
  2. 2022USB: A Unified Semi-supervised Learning Benchmark for Classificationcited by 95
  3. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  4. 2020Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networkscited by 77
  5. 2020An Analysis of Simple Data Augmentation for Named Entity Recognitioncited by 75
  6. 2021Template-free Prompt Tuning for Few-shot NERcited by 73
  7. 2020Explaining The Efficacy of Counterfactually Augmented Datacited by 64
  8. 2021CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learningcited by 64
  9. 2022Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasetscited by 54
  10. 2020Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Taskscited by 51
  11. 2021Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NERcited by 38
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  13. 2017Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networkscited by 30
  14. 2021Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysiscited by 30
  15. 2021RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Modelscited by 28
  16. 2019Label-Agnostic Sequence Labeling by Copying Nearest Neighborscited by 27
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  18. 2019Hierarchically-Refined Label Attention Network for Sequence Labelingcited by 25
  19. 2020Adaptive Self-training for Few-shot Neural Sequence Labelingcited by 18
  20. 2022Few-shot Named Entity Recognition with Self-describing Networkscited by 16
  21. 2017Transfer Learning for Named-Entity Recognition with Neural Networkscited by 15
  22. 2021Data Augmentation for Cross-Domain Named Entity Recognitioncited by 14
  23. 2019Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labelingcited by 4

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Cited by year

Citing papers per year: 2022: 1; 2023: 5; 2024: 0; 2025: 1.

7 papers.

  1. 2023Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluationscited by 96
  2. 2022GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspectivecited by 72
  3. 2023PromptNER: Prompting For Named Entity Recognitioncited by 35
  4. 2023RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbationcited by 6
  5. 2023RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answeringcited by 4
  6. 2023Data Augmentation for Robust Character Detection in Fantasy Novelscited by 1
  7. 2025CodeNER: Code Prompting for Named Entity Recognitioncited by 0