Research On Task-oriented Dialog Systems For Intelligent Tourism

With the constant advancement of technology,the development of intelligent dialogue systems with different types of functionality has significant practical significance.One of the primary goals of intelligent dialogue research is to develop large-scale task-oriented dialogue systems that are responsive,interpretable,controllable,and reliable.Currently,task-oriented dialogue systems that are practically applied are generally based on specific tasks and artificial rules,with limited scalability.Although deep learning methods have been widely applied in the academic research field to construct task-oriented dialogue systems,the algorithms have not yet been perfected,and there is still room to improve system performance.This paper is based on the collaborative project ’Natural Language Question Answering System for Tourism’,which aims to investigate algorithms that can handle multi-intent scenarios within dialogue systems.It utilizes deep learning algorithms to enhance the competency of task-oriented dialogue systems in processing intricate text.Moreover,it builds task-oriented dialogue systems using both pipeline and end-to-end models,with a focus on the practical application of dialogue systems.Firstly,we propose an algorithm for a multi-intent natural language understanding module based on a stack-propagation framework,which enables the system to extract multiple intents contained in user’s input.The model describes intent recognition as a multi-label classification problem,incorporating attention and self-attention mechanisms to capture the semantic relationships between intents and slot values.The two tasks are jointly trained,while propagating the normalization method to multi-intent scenarios to eliminate the impact of uneven distribution of intent types in the training corpus on the model.Then,the effectiveness and generalization of this model are validated through comparative evaluation with other models.Secondly,we design and implement a task-oriented multi-turn dialogue system for tourism scenarios based on a pipeline model,which aims for providing users with recommendations or queries regarding travel-related information and providing engineering support for collaborative projects.Building upon this pipeline model,developing a platform to deploy the front-end and back-end of the dialogue system,and creating a simple and convenient interface for users to achieve practical application of the system.Finally,we improve and optimize an existing model to create a practical end-to-end task-oriented dialogue system.The model is built on a Markov property-based sequence-to-sequence framework.The dialogue state tracker is explicitly modeled in the black-box model and a copying mechanism is introduced.The effectiveness of this model is verified by compparing and evaluating it against other models.On this basis,we transform and optimize the system from an engineering perspective and migrated it to the tourism scenario.To solve the issue of out-of-vocabulary words,we adopte a dynamic input vocabulary database query mechanism,enabling the system to interact with external databases and generate meaningful responses.Ultimately,we realize a practical end-to-end system application.