Parametric model of agreement

in the light of experimental data

This research is supported by Russian Science Foundation, RSF project 22-18-00037 realized at Lomonosov Moscow State University.


The project is aimed at constructing a formal model of agreement in Russian.

The problem of agreement modeling has several aspects. The first aspect is the limits of agreement as a model of syntactic interaction. The model should fully reflect the core instances of agreement as long as the relevant phenomena that show similar restrictions. The second aspect deals with the inner structure of agreement model and its parametrization: which components may be involved in the agreement relation and what characteristics they have, which features agreement relation may be established for, etc. Agreement model should be both flexible enough to allow parametric variation of the agreement mechanism based on a set of formal features, and restrictive enough to effectively limit this variation. We believe that the study of the attested variation in agreement can be a convenient research tool for identifying the parametric properties of agreement.

Variable agreement is postulated when there is an ambiguous calculation of agreement features, it occurs in situations of agreement with a non-canonical controller or in the presence of several potential agreement controllers. In addition, the constraints on agreement in various grammatical categories are not uniform. This variability is further affected by additional factors of the external syntax and lexical characteristics of the constituents involved in the agreement relation. The issue of variation puts forward increased demands towards the empirical data on which the model is based: while the modeling of canonical agreement can be done based only on qualitative (binary) differences (“grammatical / ungrammatical”), the modeling of variation that is supposed to be influenced by different factors and their interaction requires quantitative measurements.


Ekaterina Lyutikova

Project Leader

Pavel Grashchenkov

Principal Investigator

Anastasia Gerasimova

Principal Investigator

Mikhail Knyazev

Tatiana Davidjuk

Fedor Baikov

Ksenia Studenikina

Daria Belova