Abstract
In this paper, we propose a novel graph neural network with generated parameters (GPGNNs). The parameters in the propagation
module, i.e. the transition matrices used in
message passing procedure, are produced by
a generator taking natural language sentences
as inputs. We verify GP-GNNs in relation extraction from text, both on bag- and instancesettings. Experimental results on a humanannotated dataset and two distantly supervised
datasets show that multi-hop reasoning mechanism yields significant improvements. We also
perform a qualitative analysis to demonstrate
that our model could discover more accurate relations by multi-hop relational reasoning. Codes and data are released at https:
//github.com/thunlp/gp-gnn.