1. Learning Dual Retrieval Module for Semi-supervised Relation Extraction
(Me)
Hongtao Lin, Jun Yan, Meng Qu, Xiang Ren
Introduce myself (where I from), paper ideas + collaborators

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
Split unlabeled box to pseduo-labeled +

14. Proposed Model - Modules
Connect the module with details
Add more math and symbols to it

15. Proposed Model - Modules
Add box to connect the module with details
Animation of model and loss
Enlarge loss function

16. Proposed Model - Modules
Add box to connect the module with details
Animation of model and loss
Enlarge loss function

17. Proposed Model - Joint Optimization
Present the objective: PL, QL + U, ,each pointing to a module; U can be brokwn into two parts (write down formula)
Show a figure for intractable loss

18. Proposed Model - Joint Optimization
Present the objective: PL, QL + U, ,each pointing to a module; U can be brokwn into two parts (write down formula)
Show a figure for intractable loss

19. Proposed Model - Joint Optimization
Present the objective: PL, QL + U, ,each pointing to a module; U can be brokwn into two parts (write down formula)
Show a figure for intractable loss

20. Proposed Model - Joint Optimization
Present the objective: PL, QL + U, ,each pointing to a module; U can be brokwn into two parts (write down formula)
Show a figure for intractable loss

21. Proposed Model - Joint Optimization
Present the objective: PL, QL + U, ,each pointing to a module; U can be brokwn into two parts (write down formula)
Show a figure for intractable loss

22. Proposed Model - Algorithm
Step 2

23. Proposed Model - Algorithm
Step 2

24. Proposed Model - Selection Algorithm

25. Proposed Model - Selection Algorithm

26. Proposed Model - Selection Algorithm

27. Proposed Model - Algorithm
Step 2

28. Proposed Model - Instance Weighting

29. Proposed Model - Algorithm
Step 2

30. Performance Analysis - Setting
Extensive experiments on:
Two datasets (SemEval and TACRED)
Various ratios of labeled / unlabeled data
Modify the table to same style,

31. Performance Analysis - Overall Results
Change to bar chat
Add lines on data settigs
* All experiments conducted on 10% as labeled data and 50% as unlabeled data

32. Performance Analysis - Overall Results
Change to bar chat
Add lines on data settigs
* All experiments conducted on 10% as labeled data and 50% as unlabeled data

33. Performance Analysis - Overall Results
Change to bar chat
Add lines on data settigs
* All experiments conducted on 10% as labeled data and 50% as unlabeled data

34. Performance Analysis - Overall Results
Change to bar chat
Add lines on data settigs
* 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. github.com/ink-usc/DualRE
Thank you!
Contact
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.