Machine translation as one of the important applications in the field of artificial intelligence, plays a crucial role in cross-lingual communication and information transmission. However, the quality of machine translation systems directly affects the accurate conveyance and understanding of information. This research aims to investigate the translation quality of different machine translation systems in translating abstracts of foreign language and literature papers. In this study, we selected Youdao Translate, DeepL, ERNIE Bot, and ChatGPT-3.5 as the research objects for machine translation, and evaluated and analyzed the academic paper abstracts translated by these four machine translation tools, exploring their performance in translating academic paper abstracts.
Cite this paper
Jia, S. (2025). Research on Machine Translation Quality Evaluation of Academic Paper Abstracts: A Case Study of Foreign Language and Literature Papers
. Open Access Library Journal, 12, e14353. doi: http://dx.doi.org/10.4236/oalib.1114353.
Hovy, E., King, M. and Popescu-Belis, A. (2002) Principles of Context-Based Machine Transla-tion Evaluation. Machine Translation, 17, 43-75. https://doi.org/10.1023/a:1025510524115
Haque, R., Hasanuzzaman, M. and Way, A. (2020) Analysing Terminology Translation Errors in Statistical and Neural Machine Translation. Machine Translation, 34, 149-195. https://doi.org/10.1007/s10590-020-09251-z
Hudelson, P. and Chappuis, F. (2024) Using Voice-to-Voice Machine Translation to Overcome Language Barriers in Clinical Communication: An Exploratory Study. Journal of General Internal Medicine, 39, 1095-1102. https://doi.org/10.1007/s11606-024-08641-w
Koplenig, A. and Wolfer, S. (2023) Languages with More Speakers Tend to Be Harder to (Ma-chine-)Learn. Scientific Reports, 13, Article No. 18521. https://doi.org/10.1038/s41598-023-45373-z
