  Neural entity typing models typically represent entity types as vectors in a
high-dimensional space, but such spaces are not well-suited to modeling these
types' complex interdependencies. We study the ability of box embeddings, which
represent entity types as d-dimensional hyperrectangles, to represent
hierarchies of fine-grained entity type labels even when these relationships
are not defined explicitly in the ontology. Our model represents both types and
entity mentions as boxes. Each mention and its context are fed into a
BERT-based model to embed that mention in our box space; essentially, this
model leverages typological clues present in the surface text to hypothesize a
type representation for the mention. Soft box containment can then be used to
derive probabilities, both the posterior probability of a mention exhibiting a
given type and the conditional probability relations between types themselves.
We compare our approach with a strong vector-based typing model, and observe
state-of-the-art performance on several entity typing benchmarks. In addition
to competitive typing performance, our box-based model shows better performance
in prediction consistency (predicting a supertype and a subtype together) and
confidence (i.e., calibration), implying that the box-based model captures the
latent type hierarchies better than the vector-based model does.
