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Construction Grammar (CxG) is a paradigm from cognitive linguisticsemphasising the connection between syntax and semantics. Rather than rules thatoperate on lexical items, it posits constructions as the central buildingblocks of language, i.e., linguistic units of different granularity thatcombine syntax and semantics. As a first step towards assessing thecompatibility of CxG with the syntactic and semantic knowledge demonstrated bystate-of-the-art pretrained language models (PLMs), we present an investigationof their capability to classify and understand one of the most commonly studiedconstructions, the English comparative correlative (CC). We conduct experimentsexamining the classification accuracy of a syntactic probe on the one hand andthe models' behaviour in a semantic application task on the other, with BERT,RoBERTa, and DeBERTa as the example PLMs. Our results show that all threeinvestigated PLMs are able to recognise the structure of the CC but fail to useits meaning. While human-like performance of PLMs on many NLP tasks has beenalleged, this indicates that PLMs still suffer from substantial shortcomings incentral domains of linguistic knowledge.

The Better Your Syntax, the Better Your Semantics? Probing Pretrained Language Models for the English Comparative Correlative