Nowadays, enabling the transmission of knowledge and information among people belonging to different cultures is fundamental. Therefore, there is an always rising request of translations. To address this rising demand, translators in the past decades have started to rely more on tools that can aid their job: machine translation (MT) and computer-assisted translation tools (CAT), which are now essential. However, when it comes to languages that are quite distant from each other, such as Russian and Italian, finding words in the SL that do not have an attested translation in the TL can create some difficulties. In this case, one of the strategies that can be adopted is transliteration. Even MT has to perform transliteration when processing those words, such as proper names and toponyms. One of the main problems is that the outputs across various MTs do not always correspond, producing TWs with different characters that do not allow for a standardized transliteration. In this thesis an analysis on the accuracy of MT outputs is provided, so to assess which perform better among Language Waver, ModernMT, Intento, DeepL and Yandex when transliterating Russian anthroponyms and toponyms into Italian, taken from Russian passports. The metric of the assessment is based on a comparison between the output of each word generated from MTs and the norms suggested by the Russian Ministry of Foreign Affairs for anthroponyms and the UNGEGN Working Group for toponyms. Moreover, further investigation is devoted to ChatGPT-4Omni and its processing of selected STs to assess the translation outputs produced after receiving specific instructions in the prompt.

A comparative study of machine translation engines: transliterating Russian anthroponyms and toponyms into Italian

GASTALDELLO, OLGA
2023/2024

Abstract

Nowadays, enabling the transmission of knowledge and information among people belonging to different cultures is fundamental. Therefore, there is an always rising request of translations. To address this rising demand, translators in the past decades have started to rely more on tools that can aid their job: machine translation (MT) and computer-assisted translation tools (CAT), which are now essential. However, when it comes to languages that are quite distant from each other, such as Russian and Italian, finding words in the SL that do not have an attested translation in the TL can create some difficulties. In this case, one of the strategies that can be adopted is transliteration. Even MT has to perform transliteration when processing those words, such as proper names and toponyms. One of the main problems is that the outputs across various MTs do not always correspond, producing TWs with different characters that do not allow for a standardized transliteration. In this thesis an analysis on the accuracy of MT outputs is provided, so to assess which perform better among Language Waver, ModernMT, Intento, DeepL and Yandex when transliterating Russian anthroponyms and toponyms into Italian, taken from Russian passports. The metric of the assessment is based on a comparison between the output of each word generated from MTs and the norms suggested by the Russian Ministry of Foreign Affairs for anthroponyms and the UNGEGN Working Group for toponyms. Moreover, further investigation is devoted to ChatGPT-4Omni and its processing of selected STs to assess the translation outputs produced after receiving specific instructions in the prompt.
2023
A comparative study of machine translation engines: transliterating Russian anthroponyms and toponyms into Italian
machine translation
NMT
transliteration
anthroponyms
toponyms
File in questo prodotto:
File Dimensione Formato  
GASTALDELLO_OLGA.pdf

accesso aperto

Dimensione 1.05 MB
Formato Adobe PDF
1.05 MB Adobe PDF Visualizza/Apri

The text of this website © Università degli studi di Padova. Full Text are published under a non-exclusive license. Metadata are under a CC0 License

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/78819