  Morphological and syntactic changes in word usage (as captured, e.g., by
grammatical profiles) have been shown to be good predictors of a word's meaning
change. In this work, we explore whether large pre-trained contextualised
language models, a common tool for lexical semantic change detection, are
sensitive to such morphosyntactic changes. To this end, we first compare the
performance of grammatical profiles against that of a multilingual neural
language model (XLM-R) on 10 datasets, covering 7 languages, and then combine
the two approaches in ensembles to assess their complementarity. Our results
show that ensembling grammatical profiles with XLM-R improves semantic change
detection performance for most datasets and languages. This indicates that
language models do not fully cover the fine-grained morphological and syntactic
signals that are explicitly represented in grammatical profiles.
  An interesting exception are the test sets where the time spans under
analysis are much longer than the time gap between them (for example,
century-long spans with a one-year gap between them). Morphosyntactic change is
slow so grammatical profiles do not detect in such cases. In contrast, language
models, thanks to their access to lexical information, are able to detect fast
topical changes.
