Standard

Adaptation of Machine Translation Algorithms to Linguoethnic Conditions of Text Perception. / Городищев, Алексей Викторович; Городищева, Анна; Ковалев, Георгий.

2025.

Результаты исследований: Материалы конференций › материалы

Harvard

Городищев, АВ, Городищева, А & Ковалев, Г 2025, 'Adaptation of Machine Translation Algorithms to Linguoethnic Conditions of Text Perception'.

APA

Городищев, А. В., Городищева, А., & Ковалев, Г. (Принято в печать). Adaptation of Machine Translation Algorithms to Linguoethnic Conditions of Text Perception.

Vancouver

Городищев АВ, Городищева А, Ковалев Г. Adaptation of Machine Translation Algorithms to Linguoethnic Conditions of Text Perception. 2025.

Author

Городищев, Алексей Викторович ; Городищева, Анна ; Ковалев, Георгий. / Adaptation of Machine Translation Algorithms to Linguoethnic Conditions of Text Perception.

BibTeX

@conference{7864165ef95e4a8f9b36cb7804b7d305,
title = "Adaptation of Machine Translation Algorithms to Linguoethnic Conditions of Text Perception",
abstract = "The text examines the evolution of artificial intelligence (AI) technologies in the field of translation, highlighting their role in expanding capabilities for processing scientific-technical and literary texts. Despite advancements in machine translation (e.g., seq2seq and transformer models), AI's mathematical methods fundamentally differ from human approaches, leading to distortions in semantics, cultural and emotional nuances, particularly in literary texts and simultaneous translation. Limitations of AI are noted in recognizing context, emotional intensity, and culture-specific features, including idioms and metaphors. Special attention is paid to ethical issues: bias, context manipulation (e.g., Wikipedia examples), and the need to adapt models to cultural, legal, and religious databases. As a solution, the modifying the attention mechanism in transformer architectures to focus on integrating lexicographic resources and interdisciplinary data to enhance translation accuracy and ethical correctness proposed",
keywords = "artificial intelligence, MACHINE TRANSLATION, Semantic Analysis, CULTURAL CONTEXTS, ethical context of translation, attention mechanism, INTERCULTURAL COMMUNICATION, translation ethics, AI bias, simultaneous translation, large language models, Applied linguistics",
author = "Городищев, {Алексей Викторович} and Анна Городищева and Георгий Ковалев",
note = "Adaptation of Machine Translation Algorithms to Linguoethnic Conditions of Text Perception // Materials of The 9th International Leadership Conference {"}Artificial Intelligence and Leadership in Business and Economics{"} (AILBEC 2025) - https://www.prizk.cz/en/ailbec-2025/",
year = "2025",
month = jul,
day = "10",
language = "English",

}

RIS

TY - CONF

T1 - Adaptation of Machine Translation Algorithms to Linguoethnic Conditions of Text Perception

AU - Городищев, Алексей Викторович

AU - Городищева, Анна

AU - Ковалев, Георгий

N1 - Adaptation of Machine Translation Algorithms to Linguoethnic Conditions of Text Perception // Materials of The 9th International Leadership Conference "Artificial Intelligence and Leadership in Business and Economics" (AILBEC 2025) - https://www.prizk.cz/en/ailbec-2025/

PY - 2025/7/10

Y1 - 2025/7/10

N2 - The text examines the evolution of artificial intelligence (AI) technologies in the field of translation, highlighting their role in expanding capabilities for processing scientific-technical and literary texts. Despite advancements in machine translation (e.g., seq2seq and transformer models), AI's mathematical methods fundamentally differ from human approaches, leading to distortions in semantics, cultural and emotional nuances, particularly in literary texts and simultaneous translation. Limitations of AI are noted in recognizing context, emotional intensity, and culture-specific features, including idioms and metaphors. Special attention is paid to ethical issues: bias, context manipulation (e.g., Wikipedia examples), and the need to adapt models to cultural, legal, and religious databases. As a solution, the modifying the attention mechanism in transformer architectures to focus on integrating lexicographic resources and interdisciplinary data to enhance translation accuracy and ethical correctness proposed

AB - The text examines the evolution of artificial intelligence (AI) technologies in the field of translation, highlighting their role in expanding capabilities for processing scientific-technical and literary texts. Despite advancements in machine translation (e.g., seq2seq and transformer models), AI's mathematical methods fundamentally differ from human approaches, leading to distortions in semantics, cultural and emotional nuances, particularly in literary texts and simultaneous translation. Limitations of AI are noted in recognizing context, emotional intensity, and culture-specific features, including idioms and metaphors. Special attention is paid to ethical issues: bias, context manipulation (e.g., Wikipedia examples), and the need to adapt models to cultural, legal, and religious databases. As a solution, the modifying the attention mechanism in transformer architectures to focus on integrating lexicographic resources and interdisciplinary data to enhance translation accuracy and ethical correctness proposed

KW - artificial intelligence

KW - MACHINE TRANSLATION

KW - Semantic Analysis

KW - CULTURAL CONTEXTS

KW - ethical context of translation

KW - attention mechanism

KW - INTERCULTURAL COMMUNICATION

KW - translation ethics

KW - AI bias

KW - simultaneous translation

KW - large language models

KW - Applied linguistics

M3 - Paper

ER -

ID: 138414783