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Proceedings of the 28th Conference on Computational Natural Language Learning
Compiler: L. Barak, M. Alikhani
CoNLL is a conference organized yearly by SIGNLL (ACL’s Special Interest Group on Natural Language Learning), focusing on theoretically, cognitively and scientifically motivated approaches to computational linguistics. This year, CoNLL was held alongside EMNLP 2024.
Chapters
Bazhukov M., Voloshina E., Sergey Pletnev et al., , in: Proceedings of the 28th Conference on Computational Natural Language Learning.: Association for Computational Linguistics, 2024. P. 280–290.
Added: March 11, 2025
Воронеж: Издательский дом ВГУ, 2026.
В сборник вошли материалы XXXII Международной научно-технической конференции «Радиолокация, навигация, связь» (RLNC*2026), прошедшие ре цензирование членами программного комитета конференции. Основной целью конференции является организация взаимодействия научных и научно технических коллективов для обмена опытом и новыми творческими успехами. Достижение этой цели также способствует внедрению перспективных разрабо ток, имеющих практическое значение для дальнейшего развития промышленно сти, экономики ...
Added: August 29, 2026
Romanov A., Физулин А. В., Stepanyants V., Московский транспорт. Наука и проектирование 2026 № 2 С. 77–90
The behaviour of connected automated vehicles in implementing cooperative safety functions
depends on the timely exchange of V2X messages. At the same time, the end-to-end impact of 5G NR-V2X
Mode 2 message delivery failures on vehicle behaviour requires evaluation not only by aggregated network
metrics but also by the timeliness of V2X message arrival within the time window ...
Added: August 25, 2026
Trofimova E., Shamina Z., Selifanova M. et al., , in: Proceedings of the Generative Code Intelligence Workshop (GeCoIn 2026), co-located with the 35th International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2026)Vol. 4238.: CEUR-WS.org, 2026.
We introduce ML2B, the first benchmark for evaluating cross-lingual task comprehension in end-to-end ML pipeline generation by large language models. Despite growing global AI adoption, no systematic evaluation exists for ML pipeline generation beyond English task descriptions. ML2B addresses this gap with 35 Kaggle competitions spanning tabular, text, and image domains, translated into 14 languages ...
Added: August 20, 2026
Междуреченск: Кузбасский государственный технический университет им. Т.Ф. Горбачева, 2026.
В сборнике представлены материалы докладов по направлениям Международной научно-практической конференции «Современные тенденции и инновации в науке и производстве»: 1. Технологии и инновации в горной промышленности; 2. Экономика, управление, финансы; 3. Информационные системы и технологии; 4. Юный исследователь. Целью этой конференции является обмен передовым опытом, повышения квалификации их участников и, вместе с тем, это способ установления и укрепления научного сотрудничества среди ...
Added: August 11, 2026
Scientific publishing house Infinity, 2026.
These Conference Proceedings combine materials of the conference –
research papers and thesis reports of scientifi c workers. They examine technical,
juridical and sociological aspects of research issues. Some articles deal
with theoretical and methodological approaches and principles of research
questions of personality professionalization. ...
Added: July 24, 2026
Surkov A., Ignatenko V., Koltsov S., Computers, Materials and Continua 2026 Vol. 88 No. 3 Article 74
Large language models have recently demonstrated promising capabilities in mathematical reasoning; however, their performance on tasks requiring strict symbolic manipulation, such as solving differential equations, remains limited, especially for compact models. In this work, we investigate whether activation steering combined with reinforcement learning can improve the quality of solutions generated by pretrained language models without ...
Added: July 8, 2026
IEEE, 2025.
The 9th International Scientific Conference on Information, Control, and Communication Technologies (ICCT-2025) had been held October 7-11, 2025 in Gomel, Belarus. The main technical areas and applications covered by the proceedings are optoelectronics, acousto-optic, microwave technology, antenna systems, measuring technology, metamaterials, nanostructures, nanofilms, photonic crystals, biology and medicine, biophotonics, bioengineering, neural networks in communication technologies; ...
Added: June 23, 2026
Shavrina T., Fenogenova A., Emelyanov A. et al., , in: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).: Association for Computational Linguistics, 2020. P. 4717–4726.
In this paper, we introduce an advanced Russian general language understanding evaluation benchmark – RussianSuperGLUE. Recent advances in the field of universal language models and transformers require the development of a methodology for their broad diagnostics and testing for general intellectual skills - detection of natural language inference, commonsense reasoning, ability to perform simple logical ...
Added: June 14, 2026
Zmitrovich D., Abramov A., Kalmykov A. et al., , in: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).: ELRA and ICCL, 2024.
Transformer language models (LMs) are fundamental to NLP research methodologies and applications in various languages. However, developing such models specifically for the Russian language has received little attention. This paper introduces a collection of 13 Russian Transformer LMs, which spans encoder (ruBERT, ruRoBERTa, ruELECTRA), decoder (ruGPT-3), and encoder-decoder (ruT5, FRED-T5) architectures. We provide a report ...
Added: June 14, 2026
Biryukova K., Chelnokova D., Erkenova J. et al., , in: Analysis of Images, Social Networks and Texts. AIST 2024Issue 2364.: Cham: Springer, 2024. P. 109–121.
Visual Question Answering is one of the essential parts of machine reasoning. Datasets are created to train a model to perform this task. However, there are only a few datasets for the Russian language. Moreover, existing sets may have strong biases, allowing models to score high without reasoning. In this paper, we adapt the idea ...
Added: June 14, 2026
Chervyakov A., Isaeva U., Emelyanov A. et al., , in: Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)Vol. 1.: Association for Computational Linguistics, 2026. P. 2114–2161.
Multimodal large language models (MLLMs) are currently at the center of research attention, showing rapid progress in scale and capabilities, yet their intelligence, limitations, and risks remain insufficiently understood. To address these issues, particularly in the context of the Russian language, where no multimodal benchmarks currently exist, we introduce MERA Multi, an open multimodal evaluation ...
Added: June 14, 2026
Association for Computational Linguistics, 2026.
Added: June 14, 2026
Chernogorskii F., Averkiev S., Kudraleeva L. et al., , in: Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)Vol. 4.: Association for Computational Linguistics, 2026. P. 622–638.
This paper introduces DRAGOn, method to design a RAG benchmark on a regularly updated corpus. It features recent reference datasets, a question generation framework, an automatic evaluation pipeline, and a public leaderboard. Specified reference datasets allow for uniform comparison of RAG systems, while newly generated dataset versions mitigate data leakage and ensure that all models ...
Added: June 13, 2026
Kenneth E., Chung I., Kerboua I. et al., , in: Proceedings of the 13th International Conference on Learning Representations (ICLR 2025).: ICLR, 2025. P. 102004–102060.
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) - a large-scale, community-driven expansion of MTEB, covering over 500 quality-controlled evaluation tasks across 250+ languages. ...
Added: June 11, 2026
Снегирев А., Tikhonova M., Maksimova A. et al., , in: Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language TechnologiesVol. 1: Volume 1: Long Papers.: Association for Computational Linguistics, 2025. P. 236–254.
Embedding models play a crucial role in Natural Language Processing (NLP) by creating text embeddings used in various tasks such as information retrieval and assessing semantic text similarity. This paper focuses on research related to embedding models in the Russian language. It introduces a new Russian-focused embedding model called ru-en-RoSBERTa and the ruMTEB benchmark, the ...
Added: June 11, 2026
Churin I., Apishev M., Tikhonova M. et al., , in: Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025).: Suzhou: Association for Computational Linguistics, 2025. P. 1–13.
Recent progress in Natural Language Processing (NLP) has driven the creation of Large Language Models (LLMs) capable of tackling a vast range of tasks. A critical property of these models is their ability to handle large documents and process long token sequences, which has fostered the need for a robust evaluation methodology for long-text scenarios. ...
Added: June 11, 2026
Strube M., Braud C., Hardmeier C. et al., Suzhou: Association for Computational Linguistics, 2025.
Added: June 11, 2026
Plotnikov S., Альманах современной метрологии 2024 № 2(38) С. 140–149
The results of the analysis of data collected by the method of expert questioning of Russian scientific metrological institutes’ employees, as well as buyers of measuring instruments, are presented. ...
Added: May 21, 2026
Апрелев А. В., Пивоварова Н. И., Plotnikov S. et al., Альманах современной метрологии 2022 № 2(30) С. 94–101
The results of the analysis of data from the Federal Information Fund for Ensuring the Uniformity of Measurements in terms of oscilloscopes are presented. ...
Added: May 21, 2026
Rabat: Association for Computational Linguistics, 2026.
Added: May 19, 2026
Stepanyants V., Хорошилов Г. С., Долгов И. М. et al., Труды Института системного программирования РАН 2026 Т. 38 № 3 С. 95–110
Highly automated and connected vehicles are gradually entering the market. Currently, solutions are being proposed that allow these technologies to be used for cooperative driving automation, which can significantly improve traffic safety. Such technologies and their software should be tested to ensure safety before being implemented in real systems. Verification and validation of vehicular control ...
Added: May 12, 2026
Parkina U., Rakhuba M., , in: 39th Conference on Neural Information Processing Systems (NeurIPS 2025).: NeurIPS, 2025. P. 71014–71041.
Recent studies suggest that context-aware low-rank approximation is a useful tool for compression and fine-tuning of modern large-scale neural networks. In this type of approximation, a norm is weighted by a matrix of input activations, significantly improving metrics over the unweighted case. Nevertheless, existing methods for neural networks suffer from numerical instabilities due to their ...
Added: April 29, 2026
Kharitonov I. A., Springer Publishing Company, 2026.
Presents recent advances in the areas of AI, robotics, computing, electronics, security, and communications
Provides the proceedings of Future Technologies Conference 2025 (FTC 2025)
Written by experts in the fields. ...
Added: April 7, 2026
Kuvshinov A., Fominykh A., Ivanov F., IEEE Access 2026 Vol. 14 P. 50549–50557
The recursive (U|U+V) construction, a generalization of which includes polar codes, provides a powerful framework for building complex codes from simpler components. However, existing approaches predominantly rely on fixed or symmetric tree architectures, overlooking the critical impact of decomposition choice on code performance. This paper addresses the challenge of optimal tree decomposition selection by presenting a framework ...
Added: April 7, 2026