Innovations in Machine Translation: The Role of Machine Learning in Enhancing Linguistic Accuracy and Efficiency

Kaiwen Xin, Bingchen Liu and Lihao Fan

European Journal of AI, Computing & Informatics, 2025, vol. 1, issue 3, 85-92

Abstract: This essay explores Instance Induction, Analogy Induction, and Machine Learning, with a particular focus on the application of analogy-based machine learning in machine translation. It examines techniques such as full instance translation, case pattern translation, and analogical reasoning. The study investigates the underlying principles, advantages, and potential limitations of these methods to provide a theoretical foundation for further optimization of machine translation (MT). Furthermore, an in-depth analysis of Machine Learning Theory, especially through the paradigms of Analogy Induction and Instance Induction, is conducted to uncover latent patterns and features that are pivotal for the technological advancement of this field. The efficacy of these methodologies in enhancing machine translation performance is critically evaluated and discussed.

Keywords: machine translation; application of machine learning technology; artificial intelligence in Linguistics (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://pinnaclepubs.com/index.php/EJACI/article/view/357/360 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dba:ejacia:v:1:y:2025:i:3:p:85-92

Access Statistics for this article

More articles in European Journal of AI, Computing & Informatics from Pinnacle Academic Press
Bibliographic data for series maintained by Joseph Clark ().