  This work studies the semantic representations learned by BERT for compounds,
that is, expressions such as sunlight or bodyguard. We build on recent studies
that explore semantic information in Transformers at the word level and test
whether BERT aligns with human semantic intuitions when dealing with
expressions (e.g., sunlight) whose overall meaning depends -- to a various
extent -- on the semantics of the constituent words (sun, light). We leverage a
dataset that includes human judgments on two psycholinguistic measures of
compound semantic analysis: lexeme meaning dominance (LMD; quantifying the
weight of each constituent toward the compound meaning) and semantic
transparency (ST; evaluating the extent to which the compound meaning is
recoverable from the constituents' semantics). We show that BERT-based measures
moderately align with human intuitions, especially when using contextualized
representations, and that LMD is overall more predictable than ST. Contrary to
the results reported for 'standard' words, higher, more contextualized layers
are the best at representing compound meaning. These findings shed new light on
the abilities of BERT in dealing with fine-grained semantic phenomena.
Moreover, they can provide insights into how speakers represent compounds.
