Research on dynamic adaptation of English translation based on deep reinforcement learning

This research is dedicated to exploring the application of deep reinforcement learning (DRL) in dynamic adaptability of English translation. This study compared traditional sequence-to-sequence models, models using attention mechanisms, and advanced models integrating deep reinforcement learning to assess their impact on improving machine translation quality. The research found the deep reinforcement learning model significantly outperforms other models across key performance indicators like BLEU score, accuracy, and recall, particularly for complex translation tasks and maintaining contextual coherence. While deep reinforcement learning models perform well, their high computational demands and potential generalization issues also emerged as limitations. Overall, the study highlights the potential of deep reinforcement learning to adapt in dynamic environments and its promising impact on machine translation. The conclusions of this study provide strong evidence for the application of deep reinforcement learning technology in improving the performance of machine translation systems, and provide suggestions for future research directions and model optimization.