Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality

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. End-to-end task bots, typically built using a static and limited corpus, face difficulties when deployed online due to three primary factors tied to this limitation. First, they might confront queries featuring unexpected linguistic patterns or slot values (i.e., unseen user behaviors). Second, they could potentially face requirements for new functions or tasks (i.e., task definition extensions). Third, even when equipped with relevant knowledge, these bots may produce responses that appear plausible but are actually incorrect (i.e., "hallucinations"). Addressing these challenges is vital for enhancing task bots' performance and reliability in real-world settings.