Few-Shot Relation Extraction With Dual Graph Neural Network Interaction.

Area of Science:

• Natural Language Processing
• Machine Learning
• Artificial Intelligence

Background:

• Deep neural networks excel at relation extraction but require vast data and struggle with domain shifts.
• Overfitting and performance degradation in new domains are key challenges in current relation extraction models.

Purpose of the Study:

• To develop a few-shot relation extraction method that minimizes training data needs.
• To enhance domain adaptation by explicitly modeling distribution differences between datasets.

Main Methods:

• Proposing DUAL GRAPH, a graph neural network (GNN) utilizing an edge-labeling dual graph.
• The dual graph comprises an instance graph and a distribution graph to model intra/inter-class similarities and dissimilarities.
• Implementing a dual graph interaction mechanism for cyclic information fusion between graphs.

Main Results:

• DUAL GRAPH demonstrated competitive or superior performance on FewRel1.0 and FewRel2.0 benchmarks across various few-shot configurations.
• Experimental results validate the effectiveness of the proposed dual graph approach in few-shot relation extraction and domain adaptation.
• Further analysis explored parameter settings and architectural choices, providing insights into model behavior.

Conclusions:

• DUAL GRAPH offers an effective solution for few-shot relation extraction under domain adaptation.
• The approach successfully addresses the limitations of data requirements and domain shift issues in deep learning models.
• The proposed dual graph interaction mechanism enhances knowledge transfer and model robustness.