Innovations in Machine Translation: The Role of Machine Learning in Enhancing Linguistic Accuracy and Efficiency
Kaiwen Xin, Bingchen Liu, Lihao Fan · Journal of Computing and Electronic Information Management · 2024
This essay explores Instance Induction, Analogy Induction, and Machine Learning, with a specific focus on the application of analogy-based machine learning in machine translation. This includes full instance translation, case pattern translation, and analogical reasoning. The study examines the underlying principles, advantages, and potential limitations of these methods to provide a theoretical foundation for further optimizing machine translation (MT). Moreover, an in-depth examination of Machine Learning Theory, particularly through the paradigms of Analogy Induction and Instance Induction, is conducted to unearth latent patterns and features, which are pivotal for the technological evolution of this field. The efficacy of these methodologies in augmenting the performance of machine translation is critically analyzed and discussed.