Revolutionizing interpretation education: a comprehensive automatic interpretation evaluation system based on composite genetic algorithm and backpropagation neural networks

The fast progress of artificial intelligence (AI) technology has significantly impacted the field of interpretation. Nonetheless, interpretation education at many Chinese schools and universities is poor quality. It strongly emphasizes theory while providing basic content, resulting in minimal pupil growth. Furthermore, existing interpretation assessment procedures must be revaluated to improve comprehensive instructional growth. Aiming at this problem, this paper systematically constructs the Automatic Interpretation Assessment System by integrating key components. Firstly, this paper explains the significance of interpretation as a cognitive bridge between AI and human cognition by emphasizing the critical relevance of interpretability in AI systems. Second, the research explores the intricacies of the internal workings of backpropagation (BP) neural networks. It explains their structural composition, signal propagation processes, and weight adjustment procedures, all of which are strategically employed to enable optimal learning within the context of the model. Third, it integrates genetic algorithms (GA) with BP neural networks in a unique way, combining the optimization capabilities of GA. This collaborative strategy employs selection, crossover, and mutation strategies to improve system performance and create a strong issue-detection feedback mechanism. Finally, utilizing interpretable AI theory, the study methodically creates the Automatic Interpretation Assessment System. This approach has a three-tier paradigm, with careful evaluation criteria including interpreter ability and skill. The requirements include bilingual competence, extra-lingual knowledge, interpreting abilities, professional excellence, and psychological quality. We comprehensively evaluated using essential parameters such as accuracy, precision, recall, F1 score, and execution time. These results demonstrate that our proposed system excels with an accuracy of 95%, a precision of 94%, a recall of 96%, and an F1-score of 0.95, showcasing its superiority over the other systems. These findings highlight our proposed system effectiveness in reliably assessing interpretation quality and efficient processing capabilities.