Learning Dual Retrieval Module for Semi-supervised Relation Extraction

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Presentation transcript:

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

Relation Extraction

Relation Extraction

Relation Extraction

Problem Definition

Problem Definition

Problem Definition

Motivation - Related Work

Motivation - Related Work

Motivation - Related Work

Motivation - Related Work

Proposed Model - Overview

Proposed Model - Overview Split unlabeled box to pseduo-labeled +

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

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

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

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

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

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

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

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

Proposed Model - Algorithm Step 2

Proposed Model - Algorithm Step 2

Proposed Model - Selection Algorithm

Proposed Model - Selection Algorithm

Proposed Model - Selection Algorithm

Proposed Model - Algorithm Step 2

Proposed Model - Instance Weighting

Proposed Model - Algorithm Step 2

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

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

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

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

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

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

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

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

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

github.com/ink-usc/DualRE Thank you! Contact hongtao.lin@usc.edu 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.