Design and Implementation of a Task-Oriented Dialogue Model Based on Supervised Learning and Deep Reinforcement Learning
[Purposes]This study aims to explore the design of task-oriented dialogue models in intelligent conversational systems and propose a task-oriented dialogue system framework based on supervised learning and reinforcement learning.[Methods]The study adopts a combined approach of supervised learning and reinforcement learning.Firstly,the generation replies from open-domain dialogue models are incorporated into the task-oriented dialogue process,constructing a comprehensive dialogue model.Then,using methods of supervised learning and transfer learning,a dialogue policy model is constructed to guide the decision-making process of the dialogue system.Finally,deep reinforcement learning algorithms are employed for optimization and updates to enhance the performance of the dialogue system.[Findings]Experimental results demonstrate that the task-oriented dialogue system model outperforms other baseline models in evaluation metrics such as BLEU,ROUGE,and F1 scores.The model exhibits good dialogue generation capabilities and response diversity,generating accurate and diverse replies.[Conclusions]The study successfully designs an intelligent dialogue system framework based on task-oriented dialogue models by integrating supervised learning and reinforcement learning.The framework shows promising performance in task-oriented dialogue tasks,providing valuable exploration for the development of intelligent conversational systems.
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