Reinforcing personalized persuasion in task-oriented virtual sales assistant.

Area of Science:

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

Background:

• Traditional task-oriented virtual agents excel at simple tasks but fail when user goals are unpredictable or unavailable.
• Existing agents assume predictable user behavior, limiting their effectiveness in complex conversational scenarios.

Purpose of the Study:

• To propose an end-to-end framework for task-oriented persuasive dialogue generation.
• To enhance virtual agents' ability to handle challenging situations and unavailable goals through persuasion.
• To improve domain adaptation capabilities for virtual assistants.

Main Methods:

• Developed a framework combining pre-training and reinforcement learning for persuasive dialogue.
• Utilized four novel rewards to enhance response consistency and reduce repetitiveness.
• Implemented a meta-learning strategy for efficient domain adaptation.
• Curated a personalized persuasive dialogue (PPD) corpus with detailed annotations.

Main Results:

• The proposed persuasive virtual assistant significantly improves dialogue quality over traditional agents.
• The framework demonstrates enhanced consistency and repetitiveness in generated responses.
• Meta-learned models show rapid adaptation to new domains in few-shot and zero-shot settings, outperforming non-meta-learning approaches.

Conclusions:

• The developed framework effectively enhances the persuasiveness and domain adaptability of task-oriented virtual agents.
• This work represents a novel approach to improving virtual agent performance in complex and unpredictable conversational contexts.