  Prior research has explored the ability of computational models to predict a
word semantic fit with a given predicate. While much work has been devoted to
modeling the typicality relation between verbs and arguments in isolation, in
this paper we take a broader perspective by assessing whether and to what
extent computational approaches have access to the information about the
typicality of entire events and situations described in language (Generalized
Event Knowledge). Given the recent success of Transformers Language Models
(TLMs), we decided to test them on a benchmark for the \textit{dynamic
estimation of thematic fit}. The evaluation of these models was performed in
comparison with SDM, a framework specifically designed to integrate events in
sentence meaning representations, and we conducted a detailed error analysis to
investigate which factors affect their behavior. Our results show that TLMs can
reach performances that are comparable to those achieved by SDM. However,
additional analysis consistently suggests that TLMs do not capture important
aspects of event knowledge, and their predictions often depend on surface
linguistic features, such as frequent words, collocations and syntactic
patterns, thereby showing sub-optimal generalization abilities.
