Research on Adaptive Identification of English Translation Errors based on Improved Machine Learning

Successful translation error detection is significant to improve machine translation systems and aid nonnative English users. Traditional approaches are inadequate to learn from various linguistic environments and suffer from high rates of false positives and low generalizability. To overcome these obstacles, this research suggests an adaptive error detection system utilizing an enhanced hybrid machine learning approach. The model combines XGBoost, SVM, and Logistic Regression to take advantage of their complementary strengths at syntactic, semantic, and probabilistic error examination. A dynamic feature-weighting mechanism is used to provide steady performance across various domains of text, while an interpretable decision-making module ensures that the reasoning behind error classification is easily understandable. Dependence on annotated data is minimized through selfsupervised pretraining, while interpretability is upheld for real-world app. This research contributes to translation error detection by providing an efficient and scalable solution, with potential applications in AI-based language learning, multilingual education, and localization software. The system presented here is a new state-of-the-art for adaptive error detection, closing the gap between theoretical advancements and practical deployment.