Faculty of Humanities

Publications

• Book

  Edited by: M. Bergelson, L. Grenoble.

  LANGUAGE POLICY IN MULTIETHNIC COUNTRIES

  The papers in this thematic volume demonstrate that language policy in the post-Soviet
  space and elsewhere reveals a fundamental tension that mirrors global shifts: the
  conflict between state efforts to manage national identity and the organic reality of
  human communication. While regional nationalization efforts often demonstrate
  global patterns of securitization, the actual practices of speakers tell a different
  story.
  Language policy involves continuous interaction and compromises between
  different processes. In reality, multilingualism is not an ‘insecurity’ to be solved,but the normal state of affairs for both individuals and the societies that unite them.
  As demonstrated through the theory of social capital and speech accommodation
  theory, individuals naturally navigate multiple discourse communities, shifting
  between codes to build solidarity and access economic opportunities. Whether in
  the hidden multilingualism of Moscow or flourishing of the many languages of New
  York, linguistic practices are governed by a multi-scalar logic that transcends topdown
  engineering.
  By integrating the critical, structural, urban, and ecological approaches, we
  move away from deficit models like semilingualism. Instead, we recognize that
  society is held together not by a single standardized code, but by the dynamic
  interplay of diverse linguistic repertoires. The modern multiethnic countries, with
  their centrifugal models of nationalization and their complex urban hybrids, provide
  essential data for a world increasingly defined by superdiversity and neoliberal
  commodification of language. The studies presented here serve both as a record of
  our current multilingual reality and a roadmap for managing the complex ethnic and
  linguistic landscapes of the 21st century.

  The ten articles in this issue form a scholarly mosaic, sequenced to seamless
  transition from overarching theoretical and ideological frameworks to granular
  empirical analyses of sociolinguistic cases. The articles directly address the
  preceding theoretical issues, bridging the gap between historical legacies and
  current trends.
   

  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  

  2026.

• Article

  Bergelson M., Grenoble L.

  Language policy in multiethnic countries: Current trends

  This introductory article surveys current theoretical and methodological trends in language policy
  research in multilingual and multiethnic societies, with particular attention to the post-Soviet space
  and the Russian Federation. Drawing on structural, critical, ecological, and urban sociolinguistic
  approaches, the paper traces the evolution of language policy scholarship from early language
  planning models to contemporary frameworks emphasizing multilingualism, globalization, social
  inequality, and linguistic revitalization. The discussion integrates key concepts such as language
  management, linguistic markets, speech accommodation, superdiversity, and decolonial critiques of
  the “native speaker” paradigm. Particular attention is paid to the tensions between top-down state
  management and bottom-up language practices, especially in urban multilingual settings and
  endangered language communities. The growing role of language documentation and revitalization
  is also examined, highlighting current initiatives in Russia aimed at preserving linguistic diversity
  through scientifically grounded and community-centered approaches. Finally, the paper introduces
  the contributions to this special issue, which collectively explore language policy, identity,
  multilingual education, language maintenance, and revitalization across a range of global and local
  contexts. Together, the volume demonstrates that multilingualism is a normative social condition
  requiring flexible, multi-scalar, and socially responsive policy frameworks.

  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  

  Russian Journal of Linguistics. 2026. Vol. 30. No. 2. P. 275-309.

• Book chapter

  Копылова Е. В., Tsegoeva O. G., Берлин В. А. et al.

  Correcting or Rewriting? An Expert Evaluation of LLM-Based GEC on Academic Learner Data

  This paper investigates how large language models correct complex grammatical errors in Russian academic
  learner writing. Unlike traditional minimal-edit GEC systems, LLMs often apply generative rewriting strategies that
  may improve fluency, but risk structural overcorrection and semantic drift. We introduce a new expert benchmark
  derived from an authentic 3,1M-word learner corpus and construct an evaluation set annotated for error type and
  complexity.
  We propose an expert-driven evaluation framework combining quantitative scoring, structural-change analysis,
  and blind pairwise comparison. Results reveal a consistent minimal-edit vs. generative trade-off across LLMs. This
  trade-off has direct implications for evaluation, as purely reference-based metrics may underrepresent structural
  overcorrection and fail to capture differences in correction strategies.

  
  
  
  
  
  
  
  
  

  In bk.: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог». Выпуск 24.. Iss. 24. M.: Max press, 2026. P. 1-10.

• Working paper

  Zaitsev K.

  An Annotation Scheme and Classifier for Personal Facts in Dialogue

  The advancement of Large Language Models (LLMs) has enabled their application in personalized dialogue systems. We present an extended annotation scheme for personal fact classification that addresses limitations in existing approaches, particularly PeaCoK. Our scheme introduces new categories (Demographics, Possessions) and attributes (Duration, Validity, Followup) that enable structured storage, quality filtering, and identification of facts suitable for dialogue continuation. We manually annotated 2,779 facts from Multi-Session Chat and trained a multi-head classifier based on transformer encoders. Combined with the Gemma-300M encoder, the classifier achieves 81.6±2.6\% macro F1, outperforming all few-shot LLM baselines (best: GPT-5.4-mini, 72.92\%) by nearly 9 percentage points while requiring substantially fewer computational resources. Error analysis reveals persistent challenges in semantic boundary disambiguation, temporal aspect interpretation, and pragmatic reasoning for followup assessment. The dataset\footnotemark[1] and classifier\footnotemark[2] are publicly available.

  arxiv.org. Computer Science. Cornell University, 2026