Graph Neural Networks with Generated Parameters for Relation Extraction

Understand

Recently, progress has been made towards improving relational reasoning in machine learning field.

• Among existing models, graph neural networks (GNNs) is one of the most effective approaches for multi-hop relational reasoning.
• In fact, multi-hop relational reasoning is indispensable in many natural language processing tasks such as relation extraction.
• In this paper, we propose to generate the parameters of graph neural networks (GP-GNNs) according to natural language sentences, which enables GNNs to process relational reasoning on unstructured text inputs.