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Graph Neural Networks with Generated Parameters for Relation Extraction (P19-1)

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Challenge: Existing graph neural networks can only process multi-hop relational reasoning on pre-defined graphs and cannot be directly applied in natural language relational reasoning.
Approach: They propose a graph neural network with generated parameters using natural language sentences as inputs.
Outcome: The proposed model can process relational reasoning on graphs and in natural language processing tasks.

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