A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents

Area of Science:

• Artificial Intelligence
• Human-Computer Interaction
• Natural Language Processing

Background:

• Spoken dialogue systems aim for natural human-computer interaction.
• Current systems often lack dynamic adaptation to user needs.
• Personalized interactions are crucial for user satisfaction.

Purpose of the Study:

• To present a neural network framework for user intention modeling in spoken dialogue systems.
• To enable dynamic adaptation of dialogue systems based on user needs and preferences.
• To improve the success and quality of user interactions in service-oriented dialogue systems.

Main Methods:

• Developed a framework utilizing neural networks for user intention prediction.
• Integrated user intention modeling to dynamically adapt the dialogue system's behavior.
• Evaluated the framework in a user-adapted spoken dialogue system for tourist information and services.

Main Results:

• The proposed framework successfully modeled user intentions during dialogue.
• Dynamic adaptation significantly improved interaction success rates.
• Users perceived higher quality of information and services provided by the adapted system.

Conclusions:

• Neural network-based user intention modeling enhances spoken dialogue systems.
• Dynamic adaptation is key to improving user experience and service delivery.
• The framework offers a promising approach for personalized human-computer interaction.