Semantic agreement and the agreement hierarchy in large language models of Russian

Abstract

This thesis investigates the phenomenon of mixed agreement in Russian, where certain nouns denoting professions can trigger both syntactic and semantic agreement. We construct challenge sets testing different aspects of this phenomenon for pre-trained masked language models of Russian, and find that all models considered are able to model the syntactic restrictions on mixed agreement, and, to varying degrees, the preferences for semantic agreement that are observed in natural language use. We also find evidence that the models' behavior on these challenge sets is influenced by gender bias associated with the nouns in question, and that the two kinds of agreement are represented differently in the internal structure of the model.