What are the differences? A comparative study of generative artificial intelligence translation and human translation of scientific texts

Dados Bibliográficos

ID22236070
AutoresLinling Fu (South China Normal University, autor correspondente), Lei Liu (0000-0002-4422-7325, South China Normal University)
Ano2024
Volume11
Fascículo1
Data de publicação2024-09-17
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoHumanities and Social Sciences Communications (JOURNAL)
Identificadores do periódicoISSN: 2662-9992 • E-ISSN: 2662-9992
EditoraSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1057/s41599-024-03726-7
OpenAlexW4402601884
IdiomaEN
Citações recebidas9
Referências citadas50

Generative artificial intelligence translation (GenAIT) brings convenience yet also imposes severe challenges on the dissemination of knowledge. The respective (dis)advantages of GenAIT and human translation (HT), and the ways to promote their effective interaction have not been sufficiently explored yet. This study investigates the linguistic features of GenAIT and HT of scientific texts rendered from English to Chinese from lexical and syntactic levels. The GenAIT is generated by ChatGPT 3.5, a representative GenAI platform, while HTs are done by 19 Master-of-Translation-and-Interpreting students in China. Data shows that GenAIT and HTs present distinguished linguistic features in both levels. At the lexical level, HT exhibits lengthier texts with a lower average word diversity; GenAIT presents higher accuracy in translating terminology. At the syntactic level, the average sentence count in HT is greater, whereas its average sentence length measured in tokens is shorter. Moreover, human translators tend to transform sentences from passive voice into active voice more frequently than ChatGPT 3.5 does. Furthermore, human translators exhibit superior skills in deconstructing lengthy and complex sentences into shorter, more comprehensible clauses. This study reveals that ChatGPT 3.5 and human translators exhibit complementary capabilities in the translation of scientific texts, thereby suggesting an optimisation of the strengths inherent to both parties. Implications are provided for future translator training, language service providers and the ongoing development of GenAIT and HT

Generative grammar · Linguistics · Natural language processing · Chemistry · Computer Science · Natural Language Processing Techniques · Philosophy · Psychology · Topic Modeling · Translation Studies and Practices · Artificial Intelligence