  Construction Grammar (CxG) is a paradigm from cognitive linguistics
emphasising the connection between syntax and semantics. Rather than rules that
operate on lexical items, it posits constructions as the central building
blocks of language, i.e., linguistic units of different granularity that
combine syntax and semantics. As a first step towards assessing the
compatibility of CxG with the syntactic and semantic knowledge demonstrated by
state-of-the-art pretrained language models (PLMs), we present an investigation
of their capability to classify and understand one of the most commonly studied
constructions, the English comparative correlative (CC). We conduct experiments
examining the classification accuracy of a syntactic probe on the one hand and
the models' behaviour in a semantic application task on the other, with BERT,
RoBERTa, and DeBERTa as the example PLMs. Our results show that all three
investigated PLMs are able to recognise the structure of the CC but fail to use
its meaning. While human-like performance of PLMs on many NLP tasks has been
alleged, this indicates that PLMs still suffer from substantial shortcomings in
central domains of linguistic knowledge.
