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Faculty of Humanities


The Faculty of Humanities was created on December 1, 2014. It trains instructors and researchers in the field of language and literature, as well as specialists in philosophy, history, and modern culture.

The main goal of the faculty is to teach students how to understand and analyse various cultural processes, employ current research strategies, and effectively put their knowledge into practice.

The faculty’s staff are leading Russian academics and practitioners from various cultural fields, as well as invited foreign specialists. Students receive a modern education in the humanities, as well as thorough language preparation, which allows them to find extensive professional opportunities upon graduation. Students are given the opportunity to conduct research and gain practical experience at major private and public establishments.

Our strengths:

1. Interdisciplinary approach

We study the humanities alongside other academic fields so that students can apply their skills in various areas.

2. International cooperation

We maintain active international ties, which allows students to undertake internships and study abroad, as well as broaden their outlook and cultural experiences.

3. Research

We encourage and support student participation in research projects. This gives them an opportunity to apply their knowledge in practice and make a contribution to the development of the humanities.

Our graduates pursue careers in public and commercial organisations and various types of mass media. They also implement their own media, cultural, social, and educational projects.


  • Book

    Moiseev D.

    The Philosophy of Italian Fascism: Formation & Evolution

    Italian Fascism: its era has passed, yet its intellectual underpinnings remain a subject of intense scholarly debate.

    In his groundbreaking monograph, Russian scholar Dmitry Moiseev delves into the heart of Fascist political philosophy using the hermeneutical method. Tracing its roots back to the 19th-century intellectual movements that seeded its emergence, Moiseev navigates through Fascism’s ideological maturation up to its eventual demise in 1945.

    What philosophical doctrines fuelled the minds behind Italian Fascism? Did a distinct ‘Fascist philosophy’ exist, and if so, what were its core tenets? Moiseev’s work embarks on a meticulous exploration of these questions, uncovering the enduring ideas that shaped the convictions and policies of Fascist Italy’s thinkers.

    This monograph is designed for both seasoned philosophers and those intrigued by the intellectual legacy of the 20th century’s right-wing radical movements. The Philosophy of Italian Fascism is not just an academic inquiry but a journey into the ideological foundations of one of history’s most notorious regimes.

    L.: Arktos, 2024.

  • Article

    Koile E., Moroz G.

    Detecting linguistic variation with geographic sampling

    Geolectal variation is often present in settings where one language is spoken across a vast geographic area. This can be found in phonological, morphosyntactic, and lexical features. For practical reasons, it is not always possible to conduct fieldwork in every single location of interest in order to obtain the full pattern of variation, and a sample of them must be chosen. We propose and test a method for sampling these locations, with the goal of obtaining a distribution of typological features representative of the whole area. We apply k-means and hierarchical clustering algorithms for defining this sample, based on their geographic distribution. We test our methods against simulated data with several spatial configurations, and also against real data from Circassian dialects (Northwest Caucasian). Our results show an efficiency significantly higher than random sampling for detecting this variation, which makes our method profitable to fieldworkers when designing their research.

    Journal of Linguistic Geography. 2024. P. 1-8.

  • Book chapter

    Chernyavskiy A., Ostyakova L., Ilvovsky D.

    GroundHog: Dialogue Generation using Multi-Grained Linguistic Input

    Recent language models have significantly boosted conversational AI by enabling fast and cost-effective response generation in dialogue systems. However, dialogue systems based on neural generative approaches often lack truthfulness, reliability, and the ability to analyze the dialogue flow needed for smooth and consistent conversations with users. To address these issues, we introduce GroundHog, a modified BART architecture, to capture long multi-grained inputs gathered from various factual and linguistic sources, such as Abstract Meaning Representation, discourse relations, sentiment, and grounding information. For experiments, we present an automatically collected dataset from Reddit that includes multi-party conversations devoted to movies and TV series. The evaluation encompasses both automatic evaluation metrics and human evaluation. The obtained results demonstrate that using several linguistic inputs has the potential to enhance dialogue consistency, meaningfulness, and overall generation quality, even for automatically annotated data. We also provide an analysis that highlights the importance of individual linguistic features in interpreting the observed enhancements.

    In bk.: Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024). Association for Computational Linguistics, 2024. P. 149-160.

  • Working paper

    Dolgorukov V., Gladyshev M., Galimullin R.

    Dynamic Epistemic Logic of Resource Bounded Information Mining Agents

    Logics for resource-bounded agents have been getting more and more attention in recent years since they provide us with more realistic tools for modelling and reasoning about multi-agent systems. While many existing approaches are based on the idea of agents as imperfect reasoners, who must spend their resources to perform logical inference, this is not the only way to introduce resource constraints into logical settings. In this paper we study agents as perfect reasoners, who may purchase a new piece of information from a trustworthy source. For this purpose we propose dynamic epistemic logic for semi-public queries for resource-bounded agents. In this logic (groups of) agents can perform a query (ask a question) about whether some formula is true and receive a correct answer. These queries are called semi-public, because the very fact of the query is public, while the answer is private. We also assume that every query has a cost and every agent has a budget constraint. Finally, our framework allows us to reason about group queries, in which agents may share resources to obtain a new piece of information together. We demonstrate that our logic is complete, decidable and has an efficient model checking procedure.

    arxiv.org. Computer Science. Cornell University, 2024

All publications