  The goal of this study is to investigate whether a Transformer-based neural
language model infers lexical semantics and use this information for the
completion of morphosyntactic patterns. The semantic properties considered are
telicity (also combined with definiteness) and agentivity. Both act at the
interface between semantics and morphosyntax: they are semantically determined
and syntactically encoded. The tasks were submitted to both the computational
model and a group of Italian native speakers. The comparison between the two
groups of data allows us to investigate to what extent neural language models
capture significant aspects of human semantic competence.
