  Adjectives like pretty, beautiful and gorgeous describe positive properties
of the nouns they modify but with different intensity. These differences are
important for natural language understanding and reasoning. We propose a novel
BERT-based approach to intensity detection for scalar adjectives. We model
intensity by vectors directly derived from contextualised representations and
show they can successfully rank scalar adjectives. We evaluate our models both
intrinsically, on gold standard datasets, and on an Indirect Question Answering
task. Our results demonstrate that BERT encodes rich knowledge about the
semantics of scalar adjectives, and is able to provide better quality intensity
rankings than static embeddings and previous models with access to dedicated
resources.
