Specifically, we have shown that hierarchical priors allow the user enough flexibility to customize their semantics to a specific problem, while providing enough structure to resist unintended negative effects when used inappropriately.
hierarchical priors seem a natural, effective and robust choice for transferring learning across NER datasets and tasks.