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Self-distillation framework for document-level relation extraction in low-resource environments.

Hao Wu1, Gang Zhou1, Yi Xia1

  • 1Information Engineering University, Zhengzhou, Henan, China.

Peerj. Computer Science
|April 25, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a self-distillation framework to improve document-level relation extraction models. The method reduces model size and enhances performance, making it suitable for resource-limited environments.

Keywords:
Document-levelRelation extractionRow-resource environmentsSelf-distillation

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Document-level relation extraction aims to identify relationships between entities in text.
  • Deep learning models excel but often have large parameter counts, hindering deployment.
  • Resource constraints pose challenges for deploying complex deep learning models.

Purpose of the Study:

  • To propose a novel self-distillation framework for document-level relation extraction.
  • To reduce the model size of deep learning-based relation extraction systems.
  • To maintain or enhance performance while decreasing computational requirements.

Main Methods:

  • A self-distillation framework was developed for document-level relation extraction.
  • The model was partitioned into entity embedding and entity pair embedding modules.
  • Distillation techniques were applied separately to each module to reduce parameters.

Main Results:

  • The proposed framework effectively enhances performance on document-level relation extraction tasks.
  • A significant reduction in model size was achieved.
  • The approach proved effective on benchmark datasets like GDA and DocRED.

Conclusions:

  • Self-distillation offers a viable solution for creating efficient document-level relation extraction models.
  • The framework successfully balances performance enhancement with model size reduction.
  • This method facilitates the deployment of advanced relation extraction in resource-constrained settings.