Cross-Evidence Entity Relation Reasoning Model for Fact Checking
Fact checking is defined as the task of detecting false information based on evidence.To address the long-range semantic association between evidences,this paper proposes a cross-evidence entity relation reasoning model(CERM for short).The CERM model constructs a graph neural network centered on the entity relationship between evidence,and aggregates the semantic structure information of the same entity by the same entity link between dif-ferent evidence texts.Experiments on a public fact verification benchmark show that the proposed model is superior to the existing models in general evaluation indicators.
NETL
NSTL
万方数据
维普
