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

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  • Xin, Kaiwen
  • Liu, Bingchen
  • Fan, Lihao

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.

Suggested Citation

  • Xin, Kaiwen & Liu, Bingchen & Fan, Lihao, 2025. "Innovations in Machine Translation: The Role of Machine Learning in Enhancing Linguistic Accuracy and Efficiency," European Journal of AI, Computing & Informatics, Pinnacle Academic Press, vol. 1(3), pages 85-92.
    • Handle: RePEc:dba:ejacia:v:1:y:2025:i:3:p:85-92
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