  Pre-trained language models (LMs) encode rich information about linguistic
structure but their knowledge about lexical polysemy remains unclear. We
propose a novel experimental setup for analysing this knowledge in LMs
specifically trained for different languages (English, French, Spanish and
Greek) and in multilingual BERT. We perform our analysis on datasets carefully
designed to reflect different sense distributions, and control for parameters
that are highly correlated with polysemy such as frequency and grammatical
category. We demonstrate that BERT-derived representations reflect words'
polysemy level and their partitionability into senses. Polysemy-related
information is more clearly present in English BERT embeddings, but models in
other languages also manage to establish relevant distinctions between words at
different polysemy levels. Our results contribute to a better understanding of
the knowledge encoded in contextualised representations and open up new avenues
for multilingual lexical semantics research.
