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Learning Dual Retrieval Module for Semi-supervised Relation Extraction

This paper proposes a novel framework for semi-supervised relation extraction, which includes a dual task for high-quality instance retrieval. The prediction and retrieval modules are jointly trained for mutual enhancement. Extensive experiments on two datasets demonstrate consistent improvement.

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Learning Dual Retrieval Module for Semi-supervised Relation Extraction

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  1. Learning Dual Retrieval Module for Semi-supervised Relation Extraction Hongtao Lin, Jun Yan, Meng Qu, Xiang Ren (Me)

  2. Relation Extraction

  3. Relation Extraction

  4. Relation Extraction

  5. Problem Definition

  6. Problem Definition

  7. Problem Definition

  8. Motivation - Related Work

  9. Motivation - Related Work

  10. Motivation - Related Work

  11. Motivation - Related Work

  12. Proposed Model - Overview

  13. Proposed Model - Overview

  14. Proposed Model - Modules

  15. Proposed Model - Modules

  16. Proposed Model - Modules

  17. Proposed Model - Joint Optimization

  18. Proposed Model - Joint Optimization

  19. Proposed Model - Joint Optimization

  20. Proposed Model - Joint Optimization

  21. Proposed Model - Joint Optimization

  22. Proposed Model - Algorithm

  23. Proposed Model - Algorithm

  24. Proposed Model - Selection Algorithm

  25. Proposed Model - Selection Algorithm

  26. Proposed Model - Selection Algorithm

  27. Proposed Model - Algorithm

  28. Proposed Model - Instance Weighting

  29. Proposed Model - Algorithm

  30. Performance Analysis - Setting Extensive experiments on: • Two datasets (SemEval and TACRED) • Various ratios of labeled / unlabeled data

  31. Performance Analysis - Overall Results * All experiments conducted on 10% as labeled data and 50% as unlabeled data

  32. Performance Analysis - Overall Results * All experiments conducted on 10% as labeled data and 50% as unlabeled data

  33. Performance Analysis - Overall Results * All experiments conducted on 10% as labeled data and 50% as unlabeled data

  34. Performance Analysis - Overall Results * All experiments conducted on 10% as labeled data and 50% as unlabeled data

  35. Performance Analysis w.r.t. Unlabeled Data * All experiments conducted on 10% as labeled data on SemEval dataset

  36. Performance Analysis in Each Iteration * All experiments conducted on 10% as labeled data and 50% as unlabeled data on SemEval dataset

  37. Performance Analysis in Each Iteration * All experiments conducted on 10% as labeled data and 50% as unlabeled data on SemEval dataset

  38. Conclusion Proposed a novel framework for semi-supervised relation extraction task which: • Includes a dual task that retrieves high-quality instances given relation • Jointly train the prediction and retrieval modules so that they are mutually enhanced • Shows consistent improvement by extensive experiments on two datasets

  39. Contact hongtao.lin@usc.edu Thank you! Code & Data github.com/ink-usc/DualRE Acknowledgements We would like to thank all the collaborators in INK research lab for their constructive feedback on the work.

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