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DiffFSRE: Diffusion-Enhanced Prototypical Network for Few-Shot Relation Extraction.

Yang Chen1, Bowen Shi2

  • 1State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China.

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Summary
This summary is machine-generated.

This study introduces a novel diffusion model to augment data for few-shot relation extraction, improving prototypical networks by reducing overfitting and enhancing generalization to new relationships, especially in low-resource settings.

Keywords:
diffusion modelentropyfew-shot learningprototypical networksrelation extraction

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Supervised learning for relation extraction relies heavily on large datasets.
  • Few-shot relation extraction addresses data scarcity by learning from limited samples.
  • Prototypical networks are common in few-shot relation extraction but prone to overfitting.

Purpose of the Study:

  • To mitigate overfitting in prototypical networks for few-shot relation extraction.
  • To enhance generalization capabilities to unseen relation classes.
  • To leverage diffusion models for data augmentation in this domain.

Main Methods:

  • Developed a controllable conditional relation generation diffusion model for instance representation generation.
  • Proposed a pseudo-sample-enhanced prototypical network utilizing the diffusion model.
  • Introduced a pseudo-sample-aware attention mechanism with cross-entropy loss.

Main Results:

  • The proposed diffusion model-enhanced prototypical network significantly outperforms existing methods.
  • Superior performance observed particularly in low-resource, one-shot learning environments.
  • Ablation studies confirm the effectiveness and necessity of each model component.

Conclusions:

  • Diffusion model-based data augmentation effectively addresses overfitting in few-shot relation extraction.
  • The framework enhances prototype representation and generalization to unseen relations.
  • This work pioneers the use of diffusion models for data augmentation in few-shot relation extraction.