Linguopolitical challenges of Parameterization of the Russian language as a Lingua Franca: Russophony through the prism of artificial intelligence

Karabulatova, Irina Sovetovna
D.Sc. in Philology, Professor, Deputy Head of Machine Learning and Semantic Analysis, the Institute for Advanced Studies of Artificial Intelligence and Intelligent Systems, MSU,
expert of the Department of Machine Learning and Digital Humanities of MIPT University, Moscow, Russia

The modern round of cognitive psychological and information warfare has actualized the use of the resource of Russian-speaking diasporas of the post-Soviet period located in the USA, Canada and the EU countries in the confrontation between Russia and unfriendly countries. As a result, the language of the Russian-speaking abroad and the Russian-speaking diaspora becomes a trigger point for the depreciation of Russophony.
Russian as a lingua franca is characterized in such variants as: Russlinsh (mainly in the countries of the North American continent), Quelja (in German-speaking countries of Europe), Solambola (Russian-English pidgin in the north of Russia), Kyakhtin and Maymachin pidgins (in the Russian-Chinese border area).
The compilation and promotion of an information and analytical system of Russophony can become an effective response to the Western mythologeme about the dominance of the English language over all other languages. Disparate publications on Russophony need a unified systematization and creation of a unified register of Russian-foreign pidgins and Creole languages.
Detection of invariants forming Russophony is aimed at identifying directional illocution using adapted, Russified loanwords and forms. The main trend in the Russian-speaking diasporal space can be characterized as the evolutionary aspiration of the language to improve its own lexical and semantic subsystem by including and adapting the linguistic units of the contact language in the country of migration. Generalization of such material will make it possible to standardize Russophony as a superstrate with the definition of the stages of data markup and evaluation of the quality of adstrate and perstrate models in Russian-foreign language interaction. The proposed approach to the evaluation of Russophony is based on a multicode multicriteria examination.

Keywords: natural language understanding, Russophony, deep learning, text markup tasks

Acknowledgements: the research was supported by the Russian Science Foundation No. 22-18-20109,, & Krasnoyarsk Regional Fund for Support of Scientific and Scientific-technical Activities