Research And Application Of Intelligent Dialogue System Based On Knowledge Graph

Intelligent dialogue systems have been widely used in daily life,especially task-oriented dialogue systems,which are mainly used to help users complete their goals.However,the current intelligent dialogue systems are often only aimed at specific fields.Due to the lack of large-scale labeled data,it is usually difficult for such dialogue systems to feed back effective information to users only by relying on the knowledge learned by the model.Therefore,it is necessary to introduce an external knowledge base to help the dialogue system complete specific tasks,and it is also a better solution.This paper mainly involves the field of fault maintenance,and aims to establish a task-oriented dialogue system for intelligent maintenance scenarios.At present,task-oriented dialogue systems for specific domains are generally built based on pipeline methods,which mainly include three modules:natural language understanding,dialogue management,and natural language generation.In the natural language understanding module,this paper proposes a joint learning model of intent recognition and slot filling based on feature interaction.Joint modeling effectively improves the performance of the model on intent recognition and slot filling tasks,and helps the system better understand user’s intent.In the dialogue management module,this paper proposes a classification model based on supervised contrastive learning,which can effectively distinguish whether the query sentence pair matches by minimizing the inter-class variance and maximizing the between-class variance as much as possible.The best answer is selected from the returned top k candidate answers.The above knowledge base is the fault maintenance knowledge graph constructed in this paper,which is used to assist the dialogue management module to output correct responses to help users solve fault maintenance problems.Based on the above model,this paper designs and implements a task-oriented dialogue system for intelligent maintenance scenarios.The system communicates with users in natural language to help them repair faulty equipment.