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

Kaiwen Xin; Bingchen Liu; Lihao Fan

doi:10.71222/2396v567

Authors

Kaiwen Xin College of Computer and Cyber Security, Fujian Normal University, Fuzhou, Fujian, 350117, China Author
Bingchen Liu College of Computer and Cyber Security, Fujian Normal University, Fuzhou, Fujian, 350117, China Author
Lihao Fan College of Computer and Cyber Security, Fujian Normal University, Fuzhou, Fujian, 350117, China Author

DOI:

https://doi.org/10.71222/2396v567

Keywords:

machine translation, application of machine learning technology, artificial intelligence in Linguistics

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.

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