Video-language models perform far below humans on a new quadrilingual benchmark that tests understanding of action completion and duration through grammatical aspect.
Agentivit\`a e telicit\`a in GilBERTo: implicazioni cognitive
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abstract
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.
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Deep Temporal Reasoning in Video Language Models: A Cross-Linguistic Evaluation of Action Duration and Completion through Perfect Times
Video-language models perform far below humans on a new quadrilingual benchmark that tests understanding of action completion and duration through grammatical aspect.