Complexity, Comprehensibility, and Translatability from Russian: Traditional vs. Digital Methods
Reverse machine translation is an effective method of text evaluation in terms of complexity, comprehensibility, and translatability. It highlights potential translation difficulties at the pre-translation stage. Translatability and comprehensibility are juxtaposed linguacognitive concepts that can...
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Kemerovo State University
2025-04-01
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| Series: | Виртуальная коммуникация и социальные сети |
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| author | Victoria S. Melnikova |
| author_facet | Victoria S. Melnikova |
| author_sort | Victoria S. Melnikova |
| collection | DOAJ |
| description | Reverse machine translation is an effective method of text evaluation in terms of complexity, comprehensibility, and translatability. It highlights potential translation difficulties at the pre-translation stage. Translatability and comprehensibility are juxtaposed linguacognitive concepts that can correct each other in the textual and lexical dimensions. Using E. S. Klyshinsky’s linguacognitive experiment on the comprehensibility of Slavic tests by native Russian speakers, the authors checked this hypothesis using the survey method to appeal to the linguistic and metalinguistic consciousness of native speakers. They developed a new algorithm for comparing texts by reverse machine translation based on points of simplicity, complexity, clarity, and accessibility. As a digital text processing method, machine translation proved able to evaluate textual complexity, comprehensibility, and translatability. The results obtained with the new algorithm were verified with a survey, which made it possible to compare two models of understanding, i.e., that of human linguistic consciousness and that of the artificial intelligence. Alexander Pushkin’s To the Slanderers of Russia, a poetic text of high complexity, became less understandable after reverse machine translation. Comprehensibility and translatability appeared to be directly proportional. The survey yielded the same results as the reverse machine translation: the respondents and the artificial intelligence failed to understand the same words and phrases. The method of reverse machine translation proved effective in detecting textual comprehension challenges for native speakers. |
| format | Article |
| id | doaj-art-e246fa6f87024333b6ee5b7c1bfd59d2 |
| institution | OA Journals |
| issn | 2782-4799 2782-4802 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Kemerovo State University |
| record_format | Article |
| series | Виртуальная коммуникация и социальные сети |
| spelling | doaj-art-e246fa6f87024333b6ee5b7c1bfd59d22025-08-20T02:11:33ZengKemerovo State UniversityВиртуальная коммуникация и социальные сети2782-47992782-48022025-04-0141233310.21603/2782-4799-2025-4-1-23-33Complexity, Comprehensibility, and Translatability from Russian: Traditional vs. Digital MethodsVictoria S. Melnikova0https://orcid.org/0000-0002-2480-1967Kemerovo State UniversityReverse machine translation is an effective method of text evaluation in terms of complexity, comprehensibility, and translatability. It highlights potential translation difficulties at the pre-translation stage. Translatability and comprehensibility are juxtaposed linguacognitive concepts that can correct each other in the textual and lexical dimensions. Using E. S. Klyshinsky’s linguacognitive experiment on the comprehensibility of Slavic tests by native Russian speakers, the authors checked this hypothesis using the survey method to appeal to the linguistic and metalinguistic consciousness of native speakers. They developed a new algorithm for comparing texts by reverse machine translation based on points of simplicity, complexity, clarity, and accessibility. As a digital text processing method, machine translation proved able to evaluate textual complexity, comprehensibility, and translatability. The results obtained with the new algorithm were verified with a survey, which made it possible to compare two models of understanding, i.e., that of human linguistic consciousness and that of the artificial intelligence. Alexander Pushkin’s To the Slanderers of Russia, a poetic text of high complexity, became less understandable after reverse machine translation. Comprehensibility and translatability appeared to be directly proportional. The survey yielded the same results as the reverse machine translation: the respondents and the artificial intelligence failed to understand the same words and phrases. The method of reverse machine translation proved effective in detecting textual comprehension challenges for native speakers.text complexitytranslation linguisticstranslatabilityreverse machine translationcomplexitycomprehensibility |
| spellingShingle | Victoria S. Melnikova Complexity, Comprehensibility, and Translatability from Russian: Traditional vs. Digital Methods Виртуальная коммуникация и социальные сети text complexity translation linguistics translatability reverse machine translation complexity comprehensibility |
| title | Complexity, Comprehensibility, and Translatability from Russian: Traditional vs. Digital Methods |
| title_full | Complexity, Comprehensibility, and Translatability from Russian: Traditional vs. Digital Methods |
| title_fullStr | Complexity, Comprehensibility, and Translatability from Russian: Traditional vs. Digital Methods |
| title_full_unstemmed | Complexity, Comprehensibility, and Translatability from Russian: Traditional vs. Digital Methods |
| title_short | Complexity, Comprehensibility, and Translatability from Russian: Traditional vs. Digital Methods |
| title_sort | complexity comprehensibility and translatability from russian traditional vs digital methods |
| topic | text complexity translation linguistics translatability reverse machine translation complexity comprehensibility |
| work_keys_str_mv | AT victoriasmelnikova complexitycomprehensibilityandtranslatabilityfromrussiantraditionalvsdigitalmethods |