Deep Learning-Enhanced Mandarin Dialogue System: Context-Aware Management and Semantic Understanding Using BERT-SJO-DQN

Mandarin dialogue systems frequently have trouble managing context and interpreting semantics, resulting in erroneous or nonsensical responses. Many existing solutions rely on rule-based or traditional deep learning (DL) methods, which are limited in their capacity to generalize to complex, open-domain debates. This study aimed to improve dialogue management and semantic comprehension in intelligent text dialogue systems (DSs) that use Mandarin Dialogue Assistance based on DL. This study used a new Mandarin emotion conversation dataset gathered from MOVIE. Stopword systems and tokenization both use data preparation for emotional text-to-speech. We proposed bidirectional encoder representations from transformers and scheduling joint optimized with a deep Q network, using (BERT-SJO-DQN) to evaluate the effectiveness of dialogue management and semantic understanding in intelligent text dialogue systems. The model was trained and then saved for BERT fine-tuning. The transformer model is a technique that employs DL architectures. Transformers can process text-to-l dialogue language structures by using a powerful attention mechanism. Reinforcement learning, such as the DQN-DQN-based approach to dialogue management, guarantees contextually relevant and dynamic answers that consider the subtleties of user intention. Consequently, the results indicate that deep learning in Mandarin dialogue assistance performs better than comparable models regarding multi-intent accuracy, success rate, F1-score, mean average precision and recall. These research findings indicate the potential of DL to improve dialogue systems and advance the creation of strong Mandarin language assistants who can comprehend and participate in organic, context-aware dialogues.