Optimizing Student-Personalized English Translation Training Systems in Low-Resource Settings Using Meta-Learning
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Abstract
Addressing core challenges in English translation training under low-resource conditions, including resource scarcity and insufficient personalized adaptation, this study proposes a student-tailored optimization framework based on Meta-Learning. The approach is particularly applicable to specialized domains where high-quality parallel corpora are limited and efficient knowledge transfer is essential. In resource-constrained environments, conventional training systems struggle to capture individual learning characteristics and provide effective personalized services due to inadequate data availability. By exploiting the “learning to learn” capability of Meta-Learning, the proposed framework transfers cross-task knowledge to compensate for limited resources while improving adaptation efficiency. Integrating personalized learning theory with translation pedagogy, the study develops a system architecture featuring multidimensional translation competency assessment, adaptive training path generation, precise error diagnosis, and personalized resource recommendation. The optimized system rapidly adapts to students’ knowledge bases, preferences, and competency gaps without relying on large-scale datasets, thereby delivering customized translation training in low-resource settings. Beyond its educational applications, the proposed framework demonstrates the potential of data-efficient intelligent learning strategies for information processing and computational modeling in complex engineering systems, offering methodological insights for intelligent signal analysis and resource-constrained electromagnetic information processing scenarios.
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