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Publications in Math-Net.Ru
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Applying computer-assisted tools to literary translation: The case of punctuation
Inform. Primen., 18:3 (2024), 115–121
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Methodology of the corpus-based studies in the field of contrastive punctuation
Inform. Primen., 17:2 (2023), 90–95
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Integration capacities of supracorpora databases
Sistemy i Sredstva Inform., 33:1 (2023), 24–34
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The parallel corpora perspective on studying contrastive punctuation
Sistemy i Sredstva Inform., 33:1 (2023), 14–23
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Computer-assisted textual analysis in translation: Reducing the spectrum of translation models in supracorpora databases
Inform. Primen., 16:3 (2022), 68–74
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Methods of quality estimation for machine translation: State-of-the-art
Inform. Primen., 15:2 (2021), 104–111
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Expert evaluation of machine translation: Error classification
Sistemy i Sredstva Inform., 31:3 (2021), 144–157
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Indicator-based evaluation of machine translation instability
Sistemy i Sredstva Inform., 31:2 (2021), 139–151
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Reducing the spectrum of translation models in supracorpora databases
Inform. Primen., 14:2 (2020), 119–126
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Machine translation: Indicator-based evaluation of training progress in neural processing
Sistemy i Sredstva Inform., 30:4 (2020), 124–137
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Organization of the research data life cycle
Sistemy i Sredstva Inform., 30:3 (2020), 81–96
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The technique allowing for temporal estimation of machine translation instability
Sistemy i Sredstva Inform., 30:3 (2020), 67–80
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Instability of neural machine translation
Sistemy i Sredstva Inform., 30:2 (2020), 124–135
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Data life cycle planning in the (post)grant period
Sistemy i Sredstva Inform., 30:1 (2020), 135–146
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Architecture of a machine translation system
Inform. Primen., 13:3 (2019), 90–96
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Machine translation errors: Problems of classification
Sistemy i Sredstva Inform., 29:3 (2019), 92–103
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Using supracorpora databases for quantitative analysis of machine translations
Inform. Primen., 12:4 (2018), 96–105
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Machine translation of Russian connectives into French: errors and quality failures
Inform. Primen., 12:2 (2018), 105–113
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