Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality
It tackles the problem of creating more efficient and reliable dialog systems for users, but appears incremental as it reviews existing challenges without presenting new experimental results.
This thesis addresses the challenge of developing task bots that are adaptable, extensible, and accurate with minimal human intervention, exploring obstacles and potential solutions for autonomous learning in dynamic environments.
Developing adaptable, extensible, and accurate task bots with minimal or zero human intervention is a significant challenge in dialog research. This thesis examines the obstacles and potential solutions for creating such bots, focusing on innovative techniques that enable bots to learn and adapt autonomously in constantly changing environments.