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Label-Guided relation prototype generation for Continual Relation Extraction.

Shuang Liu1, XunQin Chen1, Peng Chen2

  • 1School of Computer Science and Engineering, Dalian Minzu University, Dalian, China.

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|December 9, 2024
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Summary
This summary is machine-generated.

This study introduces Label-Guided Relation Prototype Generation to improve continual relation extraction (CRE) by enhancing label semantics and preventing knowledge loss. The novel approach achieves competitive performance against state-of-the-art models.

Keywords:
Catastrophic forgettingContinual relation extraction

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Continual relation extraction (CRE) addresses evolving data streams.
  • Existing methods struggle with catastrophic forgetting and limited semantic representation in prototypes.
  • Prototype generation often overlooks rich semantic information within label namespaces.

Purpose of the Study:

  • To propose Label-Guided Relation Prototype Generation for enhanced continual relation extraction.
  • To overcome limitations of existing prototype generation methods in capturing semantic richness.
  • To mitigate catastrophic forgetting in continual learning scenarios.

Main Methods:

  • Label knowledge infusion to enrich label embedding representations.
  • Multi-head attention mechanism for diverse relation prototype generation, incorporating label semantics.
  • Multi-similarity distillation loss function to preserve prior knowledge during new task learning.

Main Results:

  • The proposed Label-Guided Relation Prototype Generation method demonstrates competitive performance.
  • The approach effectively captures diverse aspects of relation semantics.
  • Experimental results show significant improvements over baseline models in continual relation extraction.

Conclusions:

  • Label-Guided Relation Prototype Generation offers a robust solution for continual relation extraction.
  • The method successfully enhances semantic representation and knowledge retention.
  • This work advances the field of continual learning in relation extraction tasks.