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FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

arXiv 1810.10147 ↗First posted 24 Oct 2018cs.LG cs.AI cs.CLcited by 254

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Abstract (arXiv)

We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct a thorough evaluation of these methods. Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. We also show that a range of different reasoning skills are needed to solve our task. These results indicate that few-shot relation classification remains an open problem and still requires further research. Our detailed analysis points multiple directions for future research. All details and resources about the dataset and baselines are released on http://zhuhao.me/fewrel.

Key facts. Cited by 254 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Oct 2018.

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Citing papers per year: 2021: 58; 2022: 66; 2023: 53; 2024: 44; 2025: 24; 2026: 9.

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  1. 2023ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPTcited by 250
  2. 2022Is GPT-3 a Good Data Annotator?cited by 208
  3. 2021Structured Prediction as Translation between Augmented Natural Languagescited by 183
  4. 2021KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extractioncited by 180
  5. 2023InstructUIE: Multi-task Instruction Tuning for Unified Information Extractioncited by 170
  6. 2021Few-NERD: A Few-Shot Named Entity Recognition Datasetcited by 152
  7. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  8. 2021KLUE: Korean Language Understanding Evaluationcited by 114
  9. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
  10. 2022Learning from Few Examples: A Summary of Approaches to Few-Shot Learningcited by 86
  11. 2021Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extractioncited by 73
  12. 2021FLEX: Unifying Evaluation for Few-Shot NLPcited by 67
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  1. 2021CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learningcited by 64
  2. 2024Towards Lifelong Learning of Large Language Models: A Surveycited by 64
  3. 2023GPT-RE: In-context Learning for Relation Extraction using Large Language Modelscited by 62
  4. 2022RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extractioncited by 60
  5. 2022Revisiting DocRED -- Addressing the False Negative Problem in Relation Extractioncited by 57
  6. 2021Neural Data Augmentation via Example Extrapolationcited by 54
  7. 2021Few-shot Conformal Prediction with Auxiliary Taskscited by 53
  8. 2021GenIE: Generative Information Extractioncited by 53
  9. 2023A Survey on Few-Shot Class-Incremental Learningcited by 48
  10. 2023Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Textcited by 48
  11. 2021ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learningcited by 46
  12. 2024Recent Advances of Foundation Language Models-based Continual Learning: A Surveycited by 46
  13. 2022DeepStruct: Pretraining of Language Models for Structure Predictioncited by 45
  14. 2023Instruct and Extract: Instruction Tuning for On-Demand Information Extractioncited by 36
  15. 2021Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learningcited by 35
  16. 2021Curriculum-Meta Learning for Order-Robust Continual Relation Extractioncited by 34
  17. 2021Prototypical Representation Learning for Relation Extractioncited by 33
  18. 2021Knowledge Enhanced Pretrained Language Models: A Compreshensive Surveycited by 33
  19. 2021An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learningcited by 31
  20. 2022Inducing Positive Perspectives with Text Reframingcited by 31
  21. 2021Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and Systemcited by 30
  22. 2023How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?cited by 30
  23. 2023Revisiting Large Language Models as Zero-shot Relation Extractorscited by 29
  24. 2021Noisy-Labeled NER with Confidence Estimationcited by 28
  25. 2022Consistent Representation Learning for Continual Relation Extractioncited by 28
  26. 2021Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemescited by 27
  27. 2021An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labelingcited by 26
  28. 2021Visual Distant Supervision for Scene Graph Generationcited by 25
  29. 2021Exploring Task Difficulty for Few-Shot Relation Extractioncited by 24
  30. 2024ADELIE: Aligning Large Language Models on Information Extractioncited by 24
  31. 2021Revisiting Self-Training for Few-Shot Learning of Language Modelcited by 23
  32. 2021Zero-Shot Information Extraction as a Unified Text-to-Triple Translationcited by 22
  33. 2022Decomposed Meta-Learning for Few-Shot Named Entity Recognitioncited by 22
  34. 2022Label Semantics for Few Shot Named Entity Recognitioncited by 21
  35. 2022CrossRE: A Cross-Domain Dataset for Relation Extractioncited by 21
  36. 2023Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuningcited by 21
  37. 2023Zero-shot Referring Expression Comprehension via Structural Similarity Between Images and Captionscited by 21
  38. 2022MetaPrompting: Learning to Learn Better Promptscited by 19

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