mlsanityData to 7 Oct 2026

Targeted Augmentation for Low-Resource Event Extraction

arXiv 2405.08729 ↗First posted 14 May 2024cs.CL cs.AIcited by 4

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Abstract (arXiv)

Addressing the challenge of low-resource information extraction remains an ongoing issue due to the inherent information scarcity within limited training examples. Existing data augmentation methods, considered potential solutions, struggle to strike a balance between weak augmentation (e.g., synonym augmentation) and drastic augmentation (e.g., conditional generation without proper guidance). This paper introduces a novel paradigm that employs targeted augmentation and back validation to produce augmented examples with enhanced diversity, polarity, accuracy, and coherence. Extensive experimental results demonstrate the effectiveness of the proposed paradigm. Furthermore, identified limitations are discussed, shedding light on areas for future improvement.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 42 of its 69 references that are in the map. First posted on arXiv 14 May 2024.

Builds on

42 of 69 references.

References · 42

  1. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,053
  2. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  3. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 691
  4. 2019How does Disagreement Help Generalization against Label Corruption?cited by 390
  5. 2020Combating noisy labels by agreement: A joint training method with co-regularizationcited by 289
  6. 2020Frustratingly Simple Few-Shot Object Detectioncited by 280
  7. 2018Few-shot Object Detection via Feature Reweightingcited by 265
  8. 2023ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPTcited by 250
  9. 2019Meta R-CNN : Towards General Solver for Instance-level Few-shot Learningcited by 182
  10. 2020Event Extraction by Answering (Almost) Natural Questionscited by 177
  11. 2021FSCE: Few-Shot Object Detection via Contrastive Proposal Encodingcited by 168
  12. 2020Learning with Instance-Dependent Label Noise: A Sample Sieve Approachcited by 158
30 more
  1. 2020Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wildcited by 142
  2. 2023Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulnesscited by 130
  3. 2020SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustnesscited by 94
  4. 2021Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detectioncited by 80
  5. 2021Detecting Corrupted Labels Without Training a Model to Predictcited by 57
  6. 2019Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detectioncited by 54
  7. 2023An Empirical Study on Information Extraction using Large Language Modelscited by 46
  8. 2021Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherencecited by 45
  9. 2020GenAug: Data Augmentation for Finetuning Text Generatorscited by 41
  10. 2021Controllable Open-ended Question Generation with A New Question Type Ontologycited by 36
  11. 2020Semi-supervised Relation Extraction via Incremental Meta Self-Trainingcited by 35
  12. 2022Dynamic Prefix-Tuning for Generative Template-based Event Extractioncited by 35
  13. 2021Gradient Imitation Reinforcement Learning for Low Resource Relation Extractioncited by 33
  14. 2021Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifiercited by 32
  15. 2020Unsupervised Label-aware Event Trigger and Argument Classificationcited by 27
  16. 2019Reading the Manual: Event Extraction as Definition Comprehensioncited by 26
  17. 2021Query and Extract: Refining Event Extraction as Type-oriented Binary Decodingcited by 26
  18. 2021Diversifying Dialog Generation via Adaptive Label Smoothingcited by 25
  19. 2020Few-Shot Event Detection with Prototypical Amortized Conditional Random Fieldcited by 20
  20. 2020Extensively Matching for Few-shot Learning Event Detectioncited by 18
  21. 2021GTM: A Generative Triple-Wise Model for Conversational Question Generationcited by 16
  22. 2021Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classificationcited by 14
  23. 2021Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detectioncited by 13
  24. 2020SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learningcited by 11
  25. 2021ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classificationcited by 10
  26. 2021Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Interventioncited by 9
  27. 2022The Art of Prompting: Event Detection based on Type Specific Promptscited by 9
  28. 2022ExtraPhrase: Efficient Data Augmentation for Abstractive Summarizationcited by 8
  29. 2020Exploiting the Matching Information in the Support Set for Few Shot Event Classificationcited by 7
  30. 2021Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object Detectioncited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024Error-driven Data-efficient Large Multimodal Model Tuningcited by 2
  2. 2025Magic Mushroom: A Customizable Benchmark for Fine-grained Analysis of Retrieval Noise Erosion in RAG Systemscited by 1
  3. 2026Learning to Generate and Extract: A Multi-Agent Collaboration Framework For Zero-shot Document-level Event Arguments Extractioncited by 1
  4. 2025Error-Driven Scene Editing for 3D Grounding in Large Language Modelscited by 0