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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)

Issue 20: Основной том. -, 2021.

The book is a collection of papers presented as the the annual international conference “Dialogue” (2021), encompassing an extensive range of theoretical and applied research in natural language processing and modelling, as well as design of practical applications of computational linguistic technologies. The book is intended for researchers in theoretical and applied linguistics and intellectual technologies.

Chapters
‘No way!’ Discourse formulae of disagreement in Russian and English: a comparative study
Koziuk E., Badryzlova Y., , in: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)Issue 20: Основной том.: -, 2021. P. 445–455.
Added: May 11, 2021
Communication Failures in Everyday Conversations: a Case Study Based on the “Retrospective Commenting Method”
Mustajoki A., Cherkunova N., Sherstinova T., , in: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)Issue 20: Основной том.: -, 2021. P. 514–523.
The paper deals with communication failures in everyday spoken discourse. The spontaneous character of oral speech is its basic property and becomes a prerequisite for the appearance of such a phenomenon as communicative failures. By communicative failures, we mean speech situations when the recipient of a speech message does not understand it correctly, i.e., in ...
Added: August 31, 2021
Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP-models
Fenogenova A., Tikhonova M., Mikhailov V. et al., , in: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)Issue 20: Основной том.: -, 2021. Ch. 21 P. 235–245.
In the last year, new neural architectures and multilingual pre-trained models have been released for Russian, which led to performance evaluation problems across a range of language understanding tasks. This paper presents Russian SuperGLUE 1.1, an updated benchmark styled after GLUE for Russian NLP models. The new version includes a number of technical, user experience ...
Added: September 5, 2021
Using RuGPT3-XL Model for RuNormAS competition
Emelyanov A., Shliazhko O., Katricheva N. et al., , in: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)Issue 20: Основной том.: -, 2021. Ch. 18 P. 204–212.
The paper presents a fine-tuning methodology of the RuGPT3-XL (Generative Pretrained Transformer-3 for Russian) language model for the normalization of text spans task. The solution is presented in a competition for two tasks: Normalization of Named Entities (Named entities) and Normalization of a wider class of text spans, including the normalization of different parts of ...
Added: September 5, 2021
Adjunct role labeling for Russian
Kazakov R., Lyashevskaya O., , in: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)Issue 20: Основной том.: -, 2021. P. 367–377.
The task of the semantic role labeling usually focuses on identifying and classifying the core, obligatory argu­ ments of the predicate. The adjuncts of Time, Location, etc. (non­core, modifier arguments) are considered on the periphery of the task [30] and even doing the easy part of it [44], despite the fact that they are highly ...
Added: September 16, 2021
An Interpretable Approach to Lexical Semantic Change Detection with Lexical Substitution
Arefyev N.V., Bykov D. A., , in: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)Issue 20: Основной том.: -, 2021. P. 31–46.
Added: September 23, 2021
DeepMistake: Which Senses are Hard to Distinguish for a Word­-in-­Context Model
Nikolay Arefyev, Fedoseev M., Protasov V. et al., , in: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)Issue 20: Основной том.: -, 2021. P. 16–30.
In this paper, we describe our solution of the Lexical Semantic Change Detection (LSCD) problem. It is based on a Word­in­Context (WiC) model detecting whether two occurrences of a particular word carry the same meaning. We propose and compare several WiC architectures and training schemes, and also different ways to convert WiC predictions into final ...
Added: September 23, 2021
Zero-shot Cross-lingual Transfer of a Gloss Language Model for Semantic Change Detection
Rachinskiy M., Arefyev N., , in: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)Issue 20: Основной том.: -, 2021. P. 578–586.
Added: September 23, 2021
RuSimpleSentEval-2021 Shared Task: Evaluating Sentence Simplification for Russian
Sakhovskiy A., Izhevskaia A., Pestova A. et al., , in: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)Issue 20: Основной том.: -, 2021. Ch. 161 P. 607–617.
Added: September 27, 2021
Автоматическая лингвистическая разметка китайских текстов, содержащих заимствования: словоделение, транскрипция, PoS-тэггинг
Konovalova A., Volf E., Семенов К. И. et al., В кн.: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)Issue 20: Основной том.: -, 2021. С. 1081–1094.
The article tackles the problems of linguistic annotation of the Chinese texts presented in the Russian Chinese Parallel Corpus of RNC (hereafter – our corpus), and the ways to solve them. Particular attention is paid to the Rus - sian loanwords in the texts, as they, firstly, are abundant in our corpus, secondly, are of ...
Added: October 18, 2021
The order of objects in Russian: A corpus study
Bazhukov M., Chubarova L., Slioussar N. et al., , in: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)Issue 20: Основной том.: -, 2021. P. 68–78.
The paper presents the results of a corpus study of the order of direct and indirect objects in ditransitive constructions in Russian (like Petya dal Mashe yabloko ‘Petya gave Masha an apple’ or Petya dal yabloko Mashe ‘Petya gave an apple to Masha’). This topic has been widely discussed in the literature, but previous hypotheses ...
Added: October 29, 2021
Gender and case in Russian nouns denoting professions and social roles
Magomedova V., Slioussar N., , in: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)Issue 20: Основной том.: -, 2021. P. 483–491.
In the present paper, we analyzed a group of Russian nouns denoting professions and social roles. Historically, these nouns were masculine; in modern Russian, they can also be used with feminine agreement, but only nominative forms are regarded as normative (e.g. etot / eta vrač ‘thisM/F doctor’). We showed that oblique case feminine forms occur ...
Added: October 29, 2021
Detection of semantic changes in Russian nouns with distributional models and grammatical features
Ryzhova A., Ryzhova D., Sochenkov I., , in: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)Issue 20: Основной том.: -, 2021. P. 597–606.
Added: October 30, 2021
Building Dataset and Morpheme Segmentation Model for Russian Word Forms
Bolshakova E. I., Sapin A., , in: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)Issue 20: Основной том.: -, 2021. P. 154–161.
Added: October 30, 2021
Research target: Computer Science Philology and Linguistics
Priority areas: humanitarian IT and mathematics
Language: English
Keywords: computational linguisticsintellectual technologies
Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2021)
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