School of Russian
as a Foreign Language

Publications

• Book

  Birzer S., Bogomolova E., Bondarenko M. et al.

  Dynamic Teaching of Russian: Gamification of Learning

  The volume is devoted to various fields and backgrounds of Russian language instruction related to games and gamification including theory-heavy explorations, research, and submissions that focus on teaching practice.

  Routledge, 2023.

• Article

  Imbault C., Slioussar N., Ivanenko A. et al.

  Men and women are from the same planet Gender similarities in perspective-taking abilities

  The study examines emotional responses to words representing a wide range of psychological valence and focuses on gender-related differences. We aimed to find out whether men and women differ in their emotional responses, and whether they can take the perspective of another gender. We used the slider paradigm (Warriner et al., 2017): participants saw a humanoid manikin, a scale and a word on the screen and were instructed to place the manikin as close to, or as far away from, the word as they believed the person represented by the manikin would prefer to be. A change in the shape of the manikin (we used the bathroom figures of a man and a woman) signaled the perspective that the participant was asked to adopt. To assess the cross-linguistic validity of the findings, we collected data from English and Russian.

  We found that women showed a wider range of emotional responses, while men displayed a flatter affect. Participants changed their response strategy in the right direction when estimating words for the opposite gender. The study showed a degree of universality in perspective-taking, demonstrating that both men and women are aware of the emotional preferences of the opposite gender.

  The Mental Lexicon. 2026. P. 1-23.

• Book chapter

  Kopylova E.V., Tsegoeva O. G., Berlin V.A. 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. Ch. 26. P. 1-10.

• Working paper

  Remnev N., Obiedkov S., Rakhilina E. V. et al.

  A Language Model for Grammatical Error Correction in L2 Russian

  Grammatical error correction is one of the fundamental tasks in Natural Language Processing. For the Russian language, most of the spellcheckers available correct typos and other simple errors with high accuracy, but often fail when faced with non-native (L2) writing, since the latter contains errors that are not typical for native speakers. In this paper, we propose a pipeline involving a language model intended for correcting errors in L2 Russian writing. The language model proposed is trained on untagged texts of the Newspaper subcorpus of the Russian National Corpus, and the quality of the model is validated against the RULEC-GEC corpus.

  arxiv.org. Computer Science. Cornell University, 2023