The Impactful Role of ML Algo in the Field of Enactment Nostrum: An Intensive/Deep Review

Machine translation (MT) research has explored a variety of models, including statistical machine translation (SMT), rule-based machine translation (RBMT), and hybrid approaches. Hybrid MT systems aim to improve translation quality by using the strengths of different models. However, the complex set of functions associated with MT systems is still difficult to understand and optimize. This instant study propose an approach based on ML with respect to hybrid MT that addresses these issues by automatically interpreting and weighting features using ML tools. This research framework includes a classification approach to classify and compare translations from multiple black-box A system that uses ML classifiers trained on feature vectors derived from natural language processing tools. This study presents a method to train and use an SVM-based classifier to generate hybrid interpretations. The test results for English-Chinese pairs show the potential of this research approach to improve translation quality. The proposed framework is a simple and efficient way to combine different MT systems, improving translation results without manual intervention.