  We propose a novel method for hierarchical entity classification that
embraces ontological structure at both training and during prediction. At
training, our novel multi-level learning-to-rank loss compares positive types
against negative siblings according to the type tree. During prediction, we
define a coarse-to-fine decoder that restricts viable candidates at each level
of the ontology based on already predicted parent type(s). We achieve
state-of-the-art across multiple datasets, particularly with respect to strict
accuracy.
