  We study a variant of domain adaptation for named-entity recognition where
multiple, heterogeneously tagged training sets are available. Furthermore, the
test tag-set is not identical to any individual training tag-set. Yet, the
relations between all tags are provided in a tag hierarchy, covering the test
tags as a combination of training tags. This setting occurs when various
datasets are created using different annotation schemes. This is also the case
of extending a tag-set with a new tag by annotating only the new tag in a new
dataset. We propose to use the given tag hierarchy to jointly learn a neural
network that shares its tagging layer among all tag-sets. We compare this model
to combining independent models and to a model based on the multitasking
approach. Our experiments show the benefit of the tag-hierarchy model,
especially when facing non-trivial consolidation of tag-sets.
